Assessing injury risks of reclined occupants in a frontal crash preceded by braking with varied seatbelt designs using the SAFER Human Body Model

OBJECTIVE

This study investigated the effects of different seatbelt geometries and load-limiting levels on the kinematics and injury risks of a reclined occupant during a whole-sequence frontal crash scenario, using simulations with the Active SAFER Human Body Model (Active SHBM).

METHODS

The Active SHBM was positioned in a reclined position (50°) on a semi-rigid seat model. A whole-sequence frontal crash scenario, an 11 m/s2 Automated Emergency Braking (AEB) phase followed by a frontal crash at 50 km/h, was simulated. The seatbelt geometry was varied using either a B-pillar-integrated (BPI) or Belt-in-seat (BIS) design. The shoulder belt load-limiting level of the BPI seatbelt was also varied to achieve either similar shoulder belt forces (BPI_Lower_LL) or comparable upper body displacements (BPI_Higher_LL) to the BIS seatbelt. Kinematics of different body regions and seatbelt forces were compared. The risks of sustaining a mild traumatic brain injury (mTBI), two or more fractured ribs (NFR2+), and lumbar spine vertebral fractures were also compared.

RESULTS

During the pre-crash phase, head, first thoracic vertebra, and first lumbar vertebra displacements were greater with the BPI seatbelt than with the BIS, mainly due to the lack of initial contact between the torso and the seatbelt. Pelvis pre-crash displacements, however, remained consistent across seatbelt types. In the in-crash phase, variations in shoulder belt forces were directly influenced by the different load-limiting levels of the shoulder belt. The mTBI (around 20%) and NFR2+ (around 70-100%) risks were amplified with BPI seatbelts, especially at higher load-limiting force. However, the BPI design demonstrated reduced lumbar spine fracture risks (from 30% to 1%).

CONCLUSIONS

The BIS seatbelt appears promising, as seen with the reduced mTBI and NFR2+ risks, for ensuring the protection of reclined occupants in frontal crashes. However, additional solutions, such as lap belt load limiting, should be considered to reduce lumbar spine loading.

Awake at the wheel: how auto technology innovations present ongoing sleep challenges and new safety opportunities

Individuals and society are dependent on transportation. Individuals move about their world for work, school, healthcare, social activities, religious and athletic events, and so much more. Society requires the movement of goods, food, medicine, etc. for basic needs, commerce, cultural and political exchanges, and all of its dynamic, complex elements. To meet these critical daily demands, the transportation system operates globally and around the clock. Regardless of their role, a basic requirement for the individuals operating the transportation system is that they are awake and at optimal alertness. This applies to individuals driving their own cars, riding a bike or motorcycle, as well as pilots of commercial aircraft, train engineers, long-haul truck drivers, and air traffic controllers. Alert operators are a basic requirement for a safe and effective transportation system. Decades of scientific and operational research have demonstrated that the 24/7 scheduling demands on operators and passengers of our transportation system create sleep and circadian disruptions that reduce alertness and performance and cause serious safety problems. These challenges underly the longstanding interest in transportation safety by the sleep and circadian scientific community. An area currently offering perhaps the most significant opportunities and challenges in transportation safety involves vehicle technology innovations. This paper provides an overview of these latest innovations with a focus on sleep-relevant issues and opportunities. Drowsy driving is discussed, along with fatigue management in round-the-clock transportation operations. Examples of cases where technology innovations could improve or complicate sleep issues are discussed, and ongoing sleep challenges and new safety opportunities are considered.

Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers' Driving Styles

OBJECTIVE

This study investigated drivers' subjective feelings and decision making in mixed traffic by quantifying driver's driving style and type of interaction.

BACKGROUND

Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers.

METHOD

Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers' subjective feelings and decision making were collected via questionnaires.

RESULTS

Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting.

CONCLUSION

Driving style and type of interaction significantly influenced drivers' subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs.

APPLICATION

This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.

Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

OBJECTIVE

Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track.

BACKGROUND

Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk.

METHOD

Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews.

RESULTS

Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants' distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations.

CONCLUSION

The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research.

APPLICATION

Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers' situation awareness.

PRÉCIS

This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

Machines meet humans on the social road: Risk implications

Human drivers and machine drivers (i.e., automated vehicles or AVs) will share roads and interact with each other, creating mixed traffic. In this perspective, we develop two mental models about them and their social interactions, aiming to understand the risk implications of AVs and mixed traffic. Based on Mental Model I (i.e., machine drivers are superior drivers without human weaknesses), many simulation-based safety assessments, which often overlook or oversimplify human-AV social interactions, have predicted significant safety benefits when machine drivers interact with or replace human drivers. In contrast, Mental Model II considers human and machine drivers as heterogeneous and incompatible, suggesting that their interactions may lead to unexpected and occasionally negative outcomes, particularly in imminent mixed traffic. This perspective gains support from recent comparative empirical studies that employ various methods such as survey experiments, driving simulators, test-tracks, on-road observations, and AV accident analysis. These studies provide initial evidence of emerging traffic risks arising from human-AV social interactions, including human drivers' aggression and road rage toward AVs, human drivers exploiting AVs, AVs exerting negative peer influences on human drivers, and their incompatibility increasing human drivers' challenges in joining mixed traffic and thus risky behaviors. We propose specific suggestions to mitigate problematic human-AV social interactions and the associated emerging risks.

Model Predictive Controller Approach for Automated Vehicle's Path Tracking

In this paper, a model predictive control (MPC) approach for controlling automated vehicle steering during path tracking is presented. A (linear parameter-varying) LPV vehicle plant model including steering dynamics is proposed to determine the system evolution matrices. The steering dynamics are modeled in two different ways by using first-order lag and a second-order lag; the application of the first-order system resulted in a slightly more accurate path-following. Additionally, a cascade MPC structure is applied in which two MPCs are used; the second-order steering dynamics are separated from the path-following controller in a second MPC. Both steering system models and the cascade MPC are evaluated in simulation and on a test vehicle. The reference trajectory is calculated based on a fixed predefined path by transforming the necessary path segment to the vehicle ego coordinate system, thereby describing the reference for the path-following task in a novel way. The MPC method computes the optimal steering angle vector at each time step for following the path. The longitudinal dynamics is controlled separately by a PI controller. After simulation evaluation, experimental tests were conducted on a test vehicle on an asphalt surface. Both simulation and experimental results prove the effectiveness of the proposed reference definition method. The effect of the applied steering system models is evaluated. The inclusion of the steering dynamics in the prediction model resulted in a significant increase in controller performance. Finally, the computational requirements of the proposed control and modeling methods are also discussed.

A Review of the Motion Planning and Control Methods for Automated Vehicles

The motion planning and control method of automated vehicles, as the key technology of automated vehicles, directly affects the safety, comfort, and other technical indicators of vehicles. The planning module is responsible for generating a vehicle driving path. The control module is responsible for driving the vehicle. In this study, we review the main methods and achievements in motion planning and motion control for automated vehicles. The advantages and disadvantages of various planning and control methods are comparatively analyzed. Finally, some predictions and summaries based on the existing research results and trends are proposed. Through this analysis, it is believed that various types of algorithms will be further integrated in the future to complement each other's strengths and weaknesses. The next area of research will be to establish more accurate vehicle models to describe vehicle motion, improve the generalization-solving ability of algorithms, and enhance the planning and control of integrated 'human-vehicle-road' traffic systems.

Reliable and transparent in-vehicle agents lead to higher behavioral trust in conditionally automated driving systems

Trust is critical for human-automation collaboration, especially under safety-critical tasks such as driving. Providing explainable information on how the automation system reaches decisions and predictions can improve system transparency, which is believed to further facilitate driver trust and user evaluation of the automated vehicles. However, what the optimal level of transparency is and how the system communicates it to calibrate drivers' trust and improve their driving performance remain uncertain. Such uncertainty becomes even more unpredictable given that the system reliability remains dynamic due to current technological limitations. To address this issue in conditionally automated vehicles, a total of 30 participants were recruited in a driving simulator study and assigned to either a low or a high system reliability condition. They experienced two driving scenarios accompanied by two types of in-vehicle agents delivering information with different transparency types: "what"-then-wait (on-demand) and "what + why" (proactive). The on-demand agent provided some information about the upcoming event and delivered more information if prompted by the driver, whereas the proactive agent provided all information at once. Results indicated that the on-demand agent was more habitable, or naturalistic, to drivers and was perceived with faster system response speed compared to the proactive agent. Drivers under the high-reliability condition complied with the takeover request (TOR) more (if the agent was on-demand) and had shorter takeover times (in both agent conditions) compared to those under the low-reliability condition. These findings inspire how the automation system can deliver information to improve system transparency while adapting to system reliability and user evaluation, which further contributes to driver trust calibration and performance correction in future automated vehicles.

Investigating the Path Tracking Algorithm Based on BP Neural Network

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP-PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm's performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP-PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP-PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP-PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results.

Traffic Breakdown Probability Estimation for Mixed Flow of Autonomous Vehicles and Human Driven Vehicles

Automated vehicles are expected to greatly boost traffic efficiency. However, how to estimate traffic breakdown probability for the mixed flow of autonomous vehicles and human driven vehicles around ramping areas remains to be answered. In this paper, we propose a stochastic temporal queueing model to reliably depict the queue dynamics of mixed traffic flow at ramping bottlenecks. The new model is a specified Newell's car-following model that allows two kinds of vehicle velocities and first-in-first-out (FIFO) queueing behaviors. The jam queue join time is supposed to be a random variable for human driven vehicles but a constant for automated vehicles. Different from many known models, we check the occurrence of significant velocity drop along the road instead of examining the duration of the simulated jam queue so as to avoid drawing the wrong conclusions of traffic breakdown. Monte Carlo simulation results show that the generated breakdown probability curves for pure human driven vehicles agree well with empirical observations. Having noticed that various driving strategy of automated vehicles exist, we carry out further analysis to show that the chosen car-following strategy of automated vehicles characterizes the breakdown probabilities. Further tests indicate that when the penetration rate of automated vehicles is larger than 20%, the traffic breakdown probability curve of the mixed traffic will be noticeably shifted rightward, if an appropriate car-following strategy is applied. This indicates the potential benefit of automated vehicles in improving traffic efficiency.

Research on the Physics-Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles

The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the physics-intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios.

Development of a Framework for Generating Driving Safety Assessment Scenarios for Automated Vehicles

Despite the technological advances in automated driving systems, traffic accidents involving automated vehicles (AVs) continue to occur, raising concerns over the safety and reliability of automated driving. For the smooth commercialization of AVs, it is necessary to systematically assess the driving safety of AVs under the various situations that they potentially face. In this context, these various situations are mostly implemented by using systematically developed scenarios. In accordance with this need, a scenario generation framework for the assessment of the driving safety of AVs is proposed by this study. The proposed framework provides a unified form of assessment with key components for each scenario stage to facilitate systematization and objectivity. The performance of the driving safety assessment scenarios generated within the proposed framework was verified. Traffic accident report data were used for verification, and the usefulness of the proposed framework was confirmed by generating a set of scenarios, ranging from functional scenarios to test cases. The scenario generation framework proposed in this study can be used to provide sustainable scenarios. In addition, from this, it is possible to create assessment scenarios for all road types and various assessment spaces, such as simulations, proving grounds, and real roads.

Multi-Criteria Evaluation for Sorting Motion Planner Alternatives

Automated vehicles are expected to push towards the evolution of the mobility environment in the near future by increasing vehicle stability and decreasing commute time and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS), which can put their wide impact at risk, as well as their acceptance by the public. In this direction, this paper presents the application of motion planning in order to minimise motion sickness in automated vehicles. Thus, an optimal control problem is formulated through which we seek the optimum velocity profile for a predefined road path for multiple fixed journey time (JT) solutions. In this way, a Pareto Front will be generated for the conflicting objectives of MS and JT. Despite the importance of optimising both of these, the optimum velocity profile should be selected after taking into consideration additional objectives. Therefore, as the optimal control is focused on the MS minimisation, a sorting algorithm is applied to seek the optimum solution among the pareto alternatives of the fixed time solutions. The aim is that this solution will correspond to the best velocity profile that also ensures the optimum compromise between motion comfort, safety and driving behaviour, energy efficiency, journey time and riding confidence.

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

One Solution Fits All? Evaluating Different Communication Strategies of a Light-based External Human-Machine Interface for Differently Sized Automated Vehicles from a Pedestrian's Perspective

Differently sized automated vehicles (AVs) will enter the roads of tomorrow and will interact with other road users. Pedestrians as vulnerable road users heavily rely on the communication with other road users, especially for the interaction with larger vehicles, as miscommunication pose a high risk. Therefore, AVs need to provide communication abilities to safely interact with pedestrians. This study's focus was on the explicit communication which is highly relevant in low-speed and low-distance traffic scenarios to clarify misunderstandings before they result in accidents. External human-machine interfaces (eHMIs) placed on the outside of AVs can be used as a communication tool to explicitly inform the surrounding traffic environment. Although research manifested effects of vehicle size on pedestrians' perceived safety and crossing behavior, little research about the eHMI design for differently sized AVs exists. This experimental online study (N = 155) aimed at investigating the application of a light-based eHMI on two differently sized AVs (car, bus) by focusing on the overall goal of ensuring traffic safety in future traffic. The light-based eHMI showed different communication strategies, i.e., a static eHMI and three dynamic eHMIs. The results revealed that an automated car was perceived as safer and affectively rated as more positive compared to an automated bus. Nevertheless, no significant differences were found between the two AVs in terms of the eHMI communication. A dynamic eHMI was perceived as safer and evaluated affectively as more positive compared to a static eHMI or no eHMI for both AVs. In conclusion, the use of a light-based eHMI had a positive effect on pedestrians' interaction with an automated car and an automated bus and, therefore, could contribute to the overall traffic safety in this study. Implications for the design of eHMIs for differently sized AVs were discussed.

The Effect of eHMI Malfunctions on Younger and Elderly Pedestrians' Trust and Acceptance of Automated Vehicle Communication Signals

To ensure traffic flow and road safety in automated driving, external human-machine interfaces (eHMIs) could prospectively support the interaction between automated vehicles (AVs; SAE Level 3 or higher) and pedestrians if implicit communication is insufficient. Particularly elderly pedestrians (≥65 years) who are notably vulnerable in terms of traffic safety might benefit of the advantages of additional signals provided by eHMIs. Previous research showed that eHMIs were assessed as useful means of communication in AVs and were preferred over exclusively implicit communication signals. However, the attitudes of elderly users regarding technology usage and acceptance are ambiguous (i.e., less intention to use technology vs. a tendency toward overreliance on technology compared to younger users). Considering potential eHMI malfunctions, an appropriate level of trust in eHMIs is required to ensure traffic safety. So far, little research respected the impact of multiple eHMI malfunctions on participants' assessment of the system. Moreover, age effects were rarely investigated in eHMIs. In the current monitor-based study, N = 36 participants (19 younger, 17 elderly) repeatedly assessed an eHMI: During an initial measurement, when encountering a valid system and after experiencing eHMI malfunctions. Participants indicated their trust and acceptance in the eHMI, feeling of safety during the interaction and vigilance toward the eHMI. The results showed a positive effect of interacting with a valid system that acted consistently to the vehicle's movements compared to an initial assessment of the system. After experiencing eHMI malfunctions, participants' assessment of the system declined significantly. Moreover, elderly participants assessed the eHMI more positive across all conditions than younger participants did. The findings imply that participants considered the vehicle's movements as implicit communication cues in addition to the provided eHMI signals during the encounters. To support traffic safety and smooth interactions, eHMI signals are required to be in line with vehicle's movements as implicit communication cues. Moreover, the results underline the importance of calibrating an appropriate level of trust in eHMI signals. An adequate understanding of eHMI signals needs to be developed. Thereby, the requirements of different user groups should be specifically considered.

A Literature Review of Wheelchair Transportation Safety Relevant to Automated Vehicles

This literature review summarizes wheelchair transportation safety, focusing on areas pertinent to designing automated vehicles (AVs) so they can accommodate people who remain seated in their wheelchairs for travel. In these situations, it is necessary to secure the wheelchair to the vehicle and provide occupant protection with a Wheelchair Tiedown and Occupant Restraint System (WTORS). For this population to use AVs, a WTORS must be crashworthy for use in smaller vehicles, able to be used independently, and adaptable for a wide range of wheelchair types. Currently available WTORS do not have these characteristics, but a universal docking interface geometry and prototype automatic seatbelt donning systems have been developed. In the absence of government regulations that address this situation, RESNA and ISO have developed voluntary industry standards to define design and performance criteria to achieve occupant protection levels for wheelchair-seated passengers that are similar to those provided by conventional vehicle seats.

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

BACKGROUND

The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution.

METHODS

This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram-EEG, electrocardiogram-ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine.

RESULTS

The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance.

CONCLUSION

The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient's life, with the support of the e-Call system.

Application of complex neck loads to human spine at the occipital condyle joint: Implications for nonstandard postures for automated vehicles

OBJECTIVE

The automotive industry's shift toward automated vehicles allows the occupants to assume postures different from the standard upright seated position. Injury criteria assessments are needed under these nonstandard postures to advance safety. The objective of this study is to develop a new device that can position the human cadaver head-neck structures in different nonstandard pre-postures using custom devices and apply external loading anticipated in modern and future automotive and military scenarios.

METHODS

An isolated head to T1 human cadaver specimen was attached to a load cell at T1. The load cell was fixed to the top of a six-degree-of-freedom custom spinal positioning device to orient the specimen such that the occipital condyle joint was in line with the torque axis of a custom angular displacement test device. The angular device converted the linear motion of a vertically oriented electro-hydraulic piston to a torque about the occipital condyle joint of the specimen. The head was pre-rotated in the axial plane, approximately 20 degrees to the left, while maintaining the coronal alignment of the lower cervical spine. Targets were secured at the head and spine (details in the body of the manuscript), and their three-dimensional positions were measured using a seven-camera optical motion capture system. Right and then left lateral bending tests were conducted. Occipital condyle joint loads were determined from the superior load cell, and the stiffness difference between the left and right lateral bending was determined.

RESULTS

The peak coronal bending moments were 27.1 Nm and 47.6 Nm for the right and left lateral bending tests. At the time of the peak x-moment, the y moments were 1.6 and 9.1 Nm, and the z moments were 3.1 and 4.8 Nm. The head angle with respect to T1 at the time of peak x-moments was 28.1 and 27.7 deg about x, 11.0 and 11.7 deg about y, and 33.9 and 21.8 deg about z axes for the right and lateral bending tests. C1 left lateral mass fractured following the left lateral bending test.

CONCLUSIONS

The stiffness of the spine increased by approximately three times due to asymmetries in posture and loading. The present system of custom spinal positioning and angular displacement test devices and loading methodologies can be used in conjunction with a conventional piston testing apparatus to conduct additional experiments to delineate the injury patterns and mechanisms and develop injury criteria applicable to modern and future vehicle environments.

Naming is framing: The framing effect of technology name on public attitude toward automated vehicles

Vehicles with automated driving systems are called by many names, which are used interchangeably in public discourse, with different and at times misleading meanings. In two studies (total N = 908), we examined the naming effects on people's cognitive (perceived benefit and risk), affective (negative and positive affect), and behavioral responses (behavioral intention) to and trust in these vehicles in the Chinese context. Study 1 considered four names (intelligent, automated, autonomous, and driverless vehicles). Study 2 presented an identical description of vehicles with full automation and considered their five names (fully intelligent, fully automated, fully autonomous, fully driverless, and driverless vehicles). We corroborated the naming effects on affective responses and trust. The framing of "driverless vehicle" was less favorable in Study 1 but more favorable in Study 2. Technology names indirectly influenced behavioral intention through certain cognitive and affective responses. Theoretical and practical implications of our results are discussed.

Work intensification in the road transport industry: An approach to new working scenarios with automated vehicles

BACKGROUND

The deployment of automated vehicles is causing transport systems to undergo a transition period. Notwithstanding such technology advancements, the work activity in road transport remains severe in terms of working conditions, given an ever-increasing work intensification scenario.

OBJECTIVE

To analyze the drivers' point of view over factors that determine the intensification of their work, to take preventive measures for future working conditions with automated vehicles.

METHODS

A sample of 336 Portuguese professional drivers answered the Health and Work Survey.

RESULTS

Work at an intense pace (70.6%) or working beyond the assigned timetable (68.5%) were reported as conditions that may induce work intensification. The need to follow production norms/meet strict deadlines or feeling exploited at work doubles the risk of musculoskeletal disorders. Moreover, dealing with tense situations with the public, exposure to constant interruptions, and once again feeling exploited at work, are risk factors that increase, at least, four times as much the perception of generalized discouragement, anxiety, or irritability.

CONCLUSIONS

The recommendations that emerge from our findings aim at ensuring that automation does not end up becoming a new source of work intensification.

Older Adults

The diffusion of fully automated vehicles (AVs), or self-driving vehicles, is expected to provide many affordances for older adults. If older adults are not willing to use AVs, they will not be able to reap these affordances. Understanding factors related to older adults' willingness to use AVs is key to ensuring that successful strategies can be devised to promote their utilization in the future. In this study, we investigate U.S. older adults' willingness to use AVs among a large and diverse sample (N = 1,231). We assessed sociodemographic, population density, health, and attitudinal determinants of willingness to use AVs. Our binary logistic regression results showed that older adults with higher levels of educational attainment, transportation limitations, and positive attitudes toward new technology adoption were more likely to be willing to use AVs. Our study indicates that older adults' willingness to use AVs are complex and vary among U.S. older adults. Practical implications and study limitations are discussed.

Designing for the Extremes: Modeling Drivers' Response Time to Take Back Control From Automation Using Bayesian Quantile Regression

OBJECTIVE

Understanding the factors that affect drivers' response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather than focusing on the central tendency.

BACKGROUND

Drivers' responses to takeover requests can be assessed using the time it takes the driver to take over control. However, all the takeover timing studies that we could find focused on the mean response time.

METHOD

A study using an advanced driving simulator evaluated the effect of takeover request timing, event type at the onset of a takeover, and visual demand on drivers' response time. A mixed effects model was fit to the data using Bayesian quantile regression.

RESULTS

Takeover request timing, event type that precipitated the takeover, and the visual demand all affect driver response time. These factors affected the 85th percentile differently than the median. This was most evident in the revealed stopped vehicle event and conditions with a longer time budget and scenes with lower visual demand.

CONCLUSION

Because the factors affect the quantiles of the distribution differently, a focus on the mean response can misrepresent actual system performance. The 85th percentile is an important performance metric because it reveals factors that contribute to delayed responses and potentially dangerous outcomes, and it also indicates how well the system accommodates differences between drivers.

Assessing Drivers' Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

OBJECTIVE

This study examines how driving styles of fully automated vehicles affect drivers' trust using a statistical technique-the two-part mixed model-that considers the frequency and magnitude of drivers' interventions.

BACKGROUND

Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle's driving style might have an important influence.

METHOD

A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation's driving style and the person's driving style affected the frequency and magnitude of their pedal depression.

RESULTS

The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed.

CONCLUSION

Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers' trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator.

APPLICATION

We offer a measure and method for assessing driving styles.

Construct Validity and Test-Retest Reliability of the Automated Vehicle User Perception Survey

Fully automated vehicles (AVs) hold promise toward providing numerous societal benefits including reducing road fatalities. However, we are uncertain about how individuals' perceptions will influence their ability to accept and adopt AVs. The 28-item Automated Vehicle User Perception Survey (AVUPS) is a visual analog scale that was previously constructed, with established face and content validity, to assess individuals' perceptions of AVs. In this study, we examined construct validity, via exploratory factor analysis and subsequent Mokken scale analyses. Next, internal consistency was assessed via Cronbach's alpha (α) and 2-week test-retest reliability was assessed via Spearman's rho (ρ) and intraclass correlation coefficient (ICC). The Mokken scale analyses resulted in a refined 20-item AVUPS and three Mokken subscales assessing specific domains of adults' perceptions of AVs: (a) Intention to use; (b) perceived barriers; and (c) well-being. The Mokken scale analysis showed that all item-coefficients of homogeneity (H) exceeded 0.3, indicating that the items reflect a single latent variable. The AVUPS indicated a strong Mokken scale (H scale = 0.51) with excellent internal consistency (α = 0.95) and test-retest reliability (ρ = 0.76, ICC = 0.95). Similarly, the three Mokken subscales ranged from moderate to strong (range H scale = 0.47-0.66) and had excellent internal consistency (range α = 0.84-0.94) and test-retest reliability (range ICC = 0.84-0.93). The AVUPS and three Mokken subscales of AV acceptance were validated in a moderate sample size (N = 312) of adults living in the United States. Two-week test-retest reliability was established using a subset of Amazon Mechanical Turk participants (N = 84). The AVUPS, or any combination of the three subscales, can be used to validly and reliably assess adults' perceptions before and after being exposed to AVs. The AVUPS can be used to quantify adults' acceptance of fully AVs.

Risk of Injury in Moral Dilemmas With Autonomous Vehicles

As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e., framing decisions as life and death and neglecting the influence of risk of injury to the involved parties on the outcome. Here, we focus on this gap and present experimental work that systematically studies the effect of risk of injury on the decisions people make in these dilemmas. In four experiments, participants were asked to program their AVs to either save five pedestrians, which we refer to as the utilitarian choice, or save the driver, which we refer to as the nonutilitarian choice. The results indicate that most participants made the utilitarian choice but that this choice was moderated in important ways by perceived risk to the driver and risk to the pedestrians. As a second contribution, we demonstrate the value of formulating AV moral dilemmas in a game-theoretic framework that considers the possible influence of others’ behavior. In the fourth experiment, we show that participants were more (less) likely to make the utilitarian choice, the more utilitarian (nonutilitarian) other drivers behaved; furthermore, unlike the game-theoretic prediction that decision-makers inevitably converge to nonutilitarianism, we found significant evidence of utilitarianism. We discuss theoretical implications for our understanding of human decision-making in moral dilemmas and practical guidelines for the design of autonomous machines that solve these dilemmas while, at the same time, being likely to be adopted in practice.

Driver Monitoring for a Driver-Centered Design and Assessment of a Merging Assistance System Based on V2V Communications

Merging is one of the most critical scenarios that can be found in road transport. In this maneuver, the driver is subjected to a high mental load due to the large amount of information he handles, while making decisions becomes a crucial issue for their safety and those in adjacent vehicles. In previous works, it was studied how the merging maneuver affected the cognitive load required for driving by means of an eye tracking system, justifying the proposal of a driver assistance system for the merging maneuver on highways. This paper presents a merging assistance system based on communications between vehicles, which allows vehicles to share internal variables of position and speed and is implemented on a mobile device located inside the vehicle. The system algorithm decides where and when the vehicle can start the merging maneuver in safe conditions and provides the appropriate information to the driver. Parameters and driving simulator tests are used for the interface definition to develop the less intrusive and demanding one. Afterward, the system prototype was installed in a real passenger car and tests in real scenarios were conducted with several drivers to assess usability and mental load. Comparisons among alternative solutions are shown and effectiveness is assessed.

Performance Analysis of Camera-based Object Detection for Automated Vehicles

For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.

Moving Into the Loop: An Investigation of Drivers' Steering Behavior in Highly Automated Vehicles

OBJECTIVE

This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering-drivers steering when the vehicle is under automated control.

BACKGROUND

Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers' steering behavior when the automation is engaged, and steering input has no effect on the vehicle state. Such vicarious steering is a potential indicator of engagement for evaluating automated vehicles.

METHOD

A total of 32 female and 32 male drivers between 25 and 55 years of age participated in this experiment. A 2 × 2 between-subject design combined control algorithms and instructed responsibility. The control algorithms (lane centering and adaptive) were intended to convey the capability of the automation. The adaptive algorithm drifted across the lane center when latent hazards were present. The instructed levels of responsibility (driver primarily responsible and automation primarily responsible) were intended to replicate the admonitions of owners' manuals.

RESULTS

The adaptive algorithm increased vicarious steering (p < .001), but instructed responsibility did not (p = .67), and there was no interaction between the algorithm and the responsibility (p = .75). Vicarious steering was associated with an increase in transitions to manual control and glances to the road but was negatively associated with driving performance immediately after the transition to manual control.

CONCLUSION

Vicarious steering is a promising indicator of driver engagement when the vehicle is under automated control and automation algorithms can promote engagement.

Ready to bully automated vehicles on public roads?

Automated vehicles (AVs), the wide adoption of which is expected to improve traffic safety significantly, are penetrating our roads. The AVs that are testing on public roads have been bullied by human road users. We are not sure whether the bullying incidents are isolated or will be common in the future. In a cross-national survey (N = 998 drivers in China and South Korea), we developed an eleven-item bullying intention questionnaire. We assumed and confirmed that, overall, participants had a greater intention to bully machine drivers than to bully other human drivers. Compared to the Korean participants, the Chinese participants reported a greater intention to drive aggressively. The correlations of their intention to bully AVs with their attitude toward AVs and with risk-benefit perception of AVs were weak. Male participants (vs. female participants) and younger participants (vs. older participants) reported a greater intention to drive aggressively. Drivers' aggressive behaviors toward AVs might be common in the future, which might increase traffic risk and hinder the implementation of this technology.

Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human–subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.

Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.

The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study

Automated vehicles will provide greater transport convenience and interconnectivity, increase mobility options to young and elderly people, and reduce traffic congestion and emissions. However, the largest obstacle towards the deployment of automated vehicles on public roads is their safety evaluation and validation. Undeniably, the role of cameras and Artificial Intelligence-based (AI) vision is vital in the perception of the driving environment and road safety. Although a significant number of studies on the detection and tracking of vehicles have been conducted, none of them focused on the role of vertical vehicle dynamics. For the first time, this paper analyzes and discusses the influence of road anomalies and vehicle suspension on the performance of detecting and tracking driving objects. To this end, we conducted an extensive road field study and validated a computational tool for performing the assessment using simulations. A parametric study revealed the cases where AI-based vision underperforms and may significantly degrade the safety performance of AVs.

Motion sickness in passenger vehicles during test track operations

As automation transforms drivers into passengers, the deployment of automated vehicles (AVs) has the potential to greatly increase the incidence of motion sickness. A study was conducted to quantify motion sickness response of front-seat passengers performing ecologically relevant passenger activities during conditions consistent with driving on public roadways. Fifty-two adults with a large range of self-reported levels of motion sickness susceptibility and age participated in data collection on a closed test track in a passenger sedan. Motion sickness ratings increased with task vs. no-task and moderate vs. low acceleration test conditions. Increased motion sickness susceptibility was associated with higher motion sickness ratings. In comparison to older participants (age > 60), younger participants (age < 60) experienced increased motion sickness. This is the first in-vehicle study that systematically compared normative passenger activities and acceleration magnitudes typical of normative driving conditions on motion sickness response for a large, diverse sample of passengers, enabling the exploration of the effects of covariates. Practitioner summary: The data demonstrate that a relatively large range of motion sickness response can be expected to result from passengers performing visual tasks in passenger vehicles. Measurement and modelling efforts should seek to elucidate relationships among the factors contributing to motion sickness for the purpose of informing and prioritising future countermeasures for automated vehicles (AVs). Abbreviations: AV(S): automated vehicles; BMI: body mass index; BVP: blood volume pulse; EDA: electrodermal activity; FMS: fast motion sickness scale; GPS: global positioning system; IMU: inertial measurement unit; ISO: International Organization for Standardization; MISC: misery scale; MSDV: motion sickness dose value; NDS: naturalistic driving study; SAE: Society of Automotive Engineers International; UMTRI: The University of Michigan Transportation Research Institute Key Aspect of Research: Motion sickness may be an important barrier to widespread adoption of automated vehicles @UMTRI.

A Novel Method to Enable the Awareness Ability of Non-V2V-Equipped Vehicles in Vehicular Networks

Autonomous vehicles need to have sufficient perception of the surrounding environment to produce appropriate driving behavior. The Vehicle-to-Vehicle (V2V) communication technology can exchange the speed, position, direction, and other information between autonomous vehicles to improve the sensing ability of the traditional on-board sensors. For example, V2V communication technology does not have a blind spot like a conventional on-board sensor, and V2V communication is not easily affected by weather conditions. However, it is almost impossible to make every vehicle a V2V-equipped vehicle in the real environment due to reasons such as policy and user choice. Low penetration of V2V-equipped vehicles greatly reduces the performance of the traditional V2V system. In this paper, however, we propose a novel method that can extend the awareness ability of the traditional V2V system without adding much extra investment. In the traditional V2V system, only a V2V-equipped vehicle can broadcast its own location information. However, the situation is somewhat different in our V2V system. Although non-V2V-equipped vehicles cannot broadcast their own location information, we can let V2V-equipped vehicle with radar and other sensors detect the location information of the surrounding non-V2V-equipped vehicles and then broadcast it out. Therefore, we think that a non-V2V-equipped vehicle can also broadcast its own location information. In this way, we greatly extend the awareness ability of the traditional V2V system. The proposed method is validated by real experiments and simulation experiments.

Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future

In the near future, vehicles will gradually gain more autonomous functionalities. Drivers' activity will be less about driving than about monitoring intelligent systems to which driving action will be delegated. Road safety, therefore, remains dependent on the human factor and we should identify the limits beyond which driver's functional state (DFS) may no longer be able to ensure safety. Depending on the level of automation, estimating the DFS may have different targets, e.g., assessing driver's situation awareness in lower levels of automation and his ability to respond to emerging hazard or assessing driver's ability to monitor the vehicle performing operational tasks in higher levels of automation. Unfitted DFS (e.g., drowsiness) may impact the driver ability respond to taking over abilities. This paper reviews the most appropriate psychophysiological indices in naturalistic driving while considering the DFS through exogenous sensors, providing the more efficient trade-off between reliability and intrusiveness. The DFS also originates from kinematic data of the vehicle, thus providing information that indirectly relates to drivers behavior. The whole data should be synchronously processed, providing a diagnosis on the DFS, and bringing it to the attention of the decision maker in real time. Next, making the information available can be permanent or intermittent (or even undelivered), and may also depend on the automation level. Such interface can include recommendations for decision support or simply give neutral instruction. Mapping of relevant psychophysiological and behavioral indicators for DFS will enable practitioners and researchers provide reliable estimates, fitted to the level of automation.

On the future of transportation in an era of automated and autonomous vehicles

Automated vehicles (AVs) already navigate US highways and those of many other nations around the world. Current questions about AVs do not now revolve around whether such technologies should or should not be implemented; they are already with us. Rather, such questions are more and more focused on how such technologies will impact evolving transportation systems, our social world, and the individuals who live within it and whether such systems ought to be fully automated or remain under some form of direct human control. More importantly, how will mobility itself change as these independent operational vehicles first share and then dominate our roadways? How will the public be kept apprised of their evolving capacities, and what will be the impact of science and the communication of scientific advances across the varying forms of social media on these developments? We look here to address these issues and to provide some suggestions for the problems that are currently emerging.

energy system

Vehicle automation has the potential to drastically transform transportation, with important implications for energy and the environment. There is considerable uncertainty regarding the impact of automation on travel demand and vehicle efficiency. We utilize the MARKet ALlocation (MARKAL) energy system model to examine four previously published scenarios that consider different effects of automation on efficiency and demand. We do not replicate detailed estimation of individual mechanisms but apply key outcomes from prior studies within a broader energy system framework. Our analysis adds insights on fuel switching, upstream impacts, and air emissions. MARKAL dynamically captures interactions between transportation and non-transportation sectors, which is important given that the revolutionary shifts from automation may invalidate static assumptions. Model results suggest that increasing travel demands from automation may boost fuel use and petroleum-based fuel prices, potentially increasing the market penetration of alternative-fuel vehicles. In contrast, dramatic efficiency improvements from automation could drive fuel prices lower, greatly reducing the competitiveness of alternative-fueled vehicles. Furthermore, these shifts could yield positive or negative environmental impacts. Some automation scenarios even resulted in counterintuitive results. For example, if high levels of efficiency improvement drive out alternative-fuel vehicles, such as battery electric and hybrids, a net worsening of air quality relative to the other scenarios could result. We also found system-level dynamics to be key. For example, reductions in liquid fuel prices led to increased consumption, and the resulting increase in air pollutant emissions offset a portion of the potential air quality benefits of automation.

Public health, autonomous automobiles, and the rush to market

The USA has the worst motor vehicle safety problem among high-income countries and is pressing forward with the development of autonomous automobiles to address it. Government guidance and regulation, still inadequate, will be critical to the safety of the public. The analysis of this public health problem in the USA reveals the key factors that will determine the benefits and risks of autonomous vehicles around the world.

A preliminary study of human model head and neck response to frontal loading in nontraditional occupant seating configurations

OBJECTIVE

Computational human body models (HBMs) are nominally omnidirectional surrogates given their structural basis in human anatomy. As a result, such models are well suited for studies related to occupant safety in anticipated highly automated vehicles (HAVs). We utilize a well-validated HBM to study the head and neck kinematics in simulations of nontraditional occupant seating configurations.

METHODS

The GHBMC M50-O v. 4.4 HBM was gravity settled into a generic seat buck and situated in a seated posture. The model was simulated in angular increments of 15 degrees clockwise from forward facing to rear facing. A pulse of 17.0 kph (NASS median) was used in each to simulate a frontal impact for each of the 13 seating configurations. Belt anchor points were rotated with the seat; the airbag was appropriately powered based on delta-V, and was not used in rear-facing orientations. Neck forces and moments were calculated.

RESULTS

The 30-degree oblique case was found to result in the maximum neck load and sagittal moment, and thus Neck Injury Criteria (NIJ). Neck loads were minimized in the rear facing condition. The moments and loads, however, were greatest in the lateral seating configuration for these frontal crash simulations.

CONCLUSIONS

In a recent policy statement on HAVs, the NHTSA indicated that vehicle manufacturers will be expected to provide countermeasures that will fully protect occupants given any planned seating or interior configurations. Furthermore, the agency indicated that virtual tests using human models could be used to demonstrate such efficacy. While the results presented are only appropriate for comparison within this study, they do indicate that human models provide reasonable biomechanical data for nontraditional occupant seating arrangements.

Perceived safety benefits, concerns, and utility of advanced driver assistance systems among owners of ADAS-equipped vehicles

OBJECTIVE

There are many unknowns regarding drivers' use and acceptance of advanced vehicle technologies. This research aimed to examine drivers' perceptions of advanced driver assistance systems (ADAS).

METHODS

This research was conducted using structured interviews and focus groups of owners of vehicles with advanced technologies.

RESULTS

Drivers' perceptions about ADAS were mixed, but generally safety was considered to be the greatest value of the systems. There was recognition that the systems may result in overreliance and thus encourage distraction behaviors or other bad driving habits, and participants generally expressed that they were ultimately responsible for the vehicle's operation and needed to be ready to override the system if it failed.

CONCLUSIONS

The findings indicate that driver characteristics and individual factors may influence perceptions, behaviors, and interactions with safety technology, and this research is a first step toward understanding any influences. Human factors issues related to automated vehicle technologies are critical for design and deployment, including those of trust, acceptance, and understanding of systems.