Safety envelope of pedestrians upon motor vehicle conflicts identified via active avoidance behaviour

Investigation of pre-crash avoidance kinematics in pedestrians of different ages through volunteer experiment and scaling methodology

OBJECTIVE

Understanding pedestrians' pre-crash avoidance kinematics is crucial for improving the identification of potential collision areas in interactions with highly automated vehicles (HAVs). Age significantly influences pedestrian avoidance velocity and the subsequent crash risks. However, current active safety systems in HAVs often overlook the influence of pedestrians' avoidance velocity and age on imminent accidents. This study analyzes how age affects pedestrian avoidance velocity and explores the incorporation of these factors in pre-crash scenarios to identify potential collision areas between pedestrians and vehicles.

METHODS

Due to the infeasibility of measuring pedestrian avoidance behaviors in real-world pre-crash scenarios, we designed an indoor experimental platform replicating emergency crossroad scenarios to prompt subjects to mimic avoidance behaviors. 7 young and 7 middle-aged subjects participated in the experiment, resulting in a collection of 306 forward-avoidance, 297 backward-avoidance, and 42 normal-walking posture sequences. We developed a scaling approach integrating pedestrian kinematics and muscle physiology to establish a velocity-mapping relationship between young and middle-aged groups. Finally, we proposed an identification method for potential collision areas that considers pedestrians' age and avoidance velocity.

RESULTS

Middle-aged subjects required more time for natural avoidance actions averaging 0.15 s for forward and 0.25 s for backward avoidance, compared to their younger counterparts. While the forward avoidance velocity of the middle-aged subjects exhibited an average decrease of 0.3 m/s compared to young subjects, their backward avoidance velocity remained nearly identical. Overall, middle-aged subjects have a larger potential collision area than young participants. Pedestrians who actively avoid vehicles have a smaller potential collision area compared to those who remain normal walking.

CONCLUSIONS

We developed an indoor simulated pre-crash scenario experiment and a scaling approach to reveal the correlation between pedestrian avoidance velocity and age. This method can be further applied to obtain the avoidance velocity of elderly pedestrians. Additionally, we validate the effect of these factors in assessing potential collision areas. The decrease in avoidance velocity highlights a larger potential collision area for middle-aged pedestrians when interacting with vehicles. Such facts and data shall be appropriately considered in developing intelligent protection systems for pedestrians.

Measuring and Predicting Multisensory Reaction Latency: A Probabilistic Model for Visual-Auditory Integration

Virtual/augmented reality (VR/AR) devices offer both immersive imagery and sound. With those wide-field cues, we can simultaneously acquire and process visual and auditory signals to quickly identify objects, make decisions, and take action. While vision often takes precedence in perception, our visual sensitivity degrades in the periphery. In contrast, auditory sensitivity can exhibit an opposite trend due to the elevated interaural time difference. What occurs when these senses are simultaneously integrated, as is common in VR applications such as 360° video watching and immersive gaming? We present a computational and probabilistic model to predict VR users' reaction latency to visual-auditory multisensory targets. To this aim, we first conducted a psychophysical experiment in VR to measure the reaction latency by tracking the onset of eye movements. Experiments with numerical metrics and user studies with naturalistic scenarios showcase the model's accuracy and generalizability. Lastly, we discuss the potential applications, such as measuring the sufficiency of target appearance duration in immersive video playback, and suggesting the optimal spatial layouts for AR interface design.

Pedestrians' safety using projected time-to-collision to electric scooters

Safety concern among electric scooter riders drives them onto sidewalks, endangering pedestrians and making them uncomfortable. Regulators' solutions are inconsistent and conflicting worldwide. Widely accepted pedestrian safety metrics may lead to converging solutions. Adapting the time-to-collision from car traffic safety, we define projected time-to-collision and experimentally study pedestrians' objective and subjective safety. We design isolated and crowd experiments using e-scooter-to-pedestrian interactions to assess the impact of various factors on objective safety. In addition, we conducted a pedestrian survey to relate the subjective safety and the metric. We report a strong correlation between subjective safety and the projected time-to-collision when agents face each other and no relation when the e-scooter overtakes a pedestrian. As a near-miss metric correlated with pedestrian comfort, projected time-to-collision is implementable in policy-making, urban architecture, and e-scooter design to enhance pedestrian safety.

ARAware: Assisting Visually Impaired People with Real-Time Critical Moving Object Identification

Autonomous outdoor moving objects like cars, motorcycles, bicycles, and pedestrians present different risks to the safety of Visually Impaired People (VIPs). Consequently, many camera-based VIP mobility assistive solutions have resulted. However, they fail to guarantee VIP safety in practice, i.e., they cannot effectively prevent collisions with more dangerous threats moving at higher speeds, namely, Critical Moving Objects (CMOs). This paper presents the first practical camera-based VIP mobility assistant scheme, ARAware, that effectively identifies CMOs in real-time to give the VIP more time to avoid danger through simultaneously addressing CMO identification, CMO risk level evaluation and classification, and prioritised CMO warning notification. Experimental results based on our real-world prototype demonstrate that ARAware accurately identifies CMOs (with 97.26% mAR and 88.20% mAP) in real-time (with a 32 fps processing speed for 30 fps incoming video). It precisely classifies CMOs according to their risk levels (with 100% mAR and 91.69% mAP), and warns in a timely manner about high-risk CMOs while effectively reducing false alarms by postponing the warning of low-risk CMOs. Compared to the closest state-of-the-art approach, DEEP-SEE, ARAware achieves significantly higher CMO identification accuracy (by 42.62% in mAR and 10.88% in mAP), with a 93% faster end-to-end processing speed.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

Insights into vehicle conflicts based on traffic flow dynamics

The utilization of traffic conflict indicators is crucial for assessing traffic safety, especially when the crash data is unavailable. To identify traffic conflicts based on traffic flow characteristics across various traffic states, we propose a framework that utilizes unsupervised learning to automatically establish surrogate safety measures (SSM) thresholds. Different traffic states and corresponding transitions are identified with the three-phase traffic theory using high-resolution trajectory data. Meanwhile, the SSMs are mapped to the corresponding traffic states from the perspectives of time, space, and deceleration. Three models, including k-means, GMM, and Mclust, are investigated and compared to optimize the identification of traffic conflicts. It is observed that Mclust outperforms the others based on the evaluation metrics. According to the results, there is a variation in the distribution of traffic conflicts among different traffic states, wide moving jam (phase J) has the highest conflict risk, followed by synchronous flow (phase S), and free flow (phase F). Meanwhile, the thresholds of traffic conflicts cannot be fully represented by the same value through different traffic states. It reveals that the heterogeneity of thresholds is exhibited across traffic state transitions, which justifies the necessity of dynamic thresholds for traffic conflict analysis.

Study on driver's active emergency response in dangerous traffic scenes based on driving simulator

OBJECTIVE

A driver's active emergency response in dangerous traffic scenes consists of two parts, including reaction behavior and physiological state. In dangerous traffic scenes, the driver's active emergency response has an important impact on human collision injury. Clarifying the driver's active emergency response is an important prerequisite for the study of human collision injury under nonstandard posture. Therefore, this study investigates the driver's active emergency response in different inevitable collision scenes using driving simulator.

METHODS

A driving simulator with a high-speed camera system and human physiological signal acquisition system was first built. Then, three typical vehicle dangerous collision scenes were developed, including frontal collision, side collision, and rear-end collision. Finally, twenty participants (15 males and 5 females) were recruited for a driving experiment, and their active emergency responses were recorded and analyzed.

RESULTS

All subjects would rotate the steering wheel to the left or right in the active emergency state, and the rotation of the hand would also cause the subject's upper body to tilt in the same direction. The maximum angle for male subjects to rotate the steering wheel was 59.98°, while for the female subjects, it was 44.28°. In addition, the maximum grip force between the male subjects and the steering wheel was 280.5 N, compared to 192.5 N for female subjects. Compared to the female participants, the male participants not only have a greater rotation angle and a greater grip force on the steering wheel, but also have greater pressure on the brake pedal, and the foot moves quickly from the accelerator pedal to the brake pedal and presses the brake pedal.

CONCLUSIONS

Drivers have different active emergency responses to different vehicle collision scenes. Quantitative statistics of driver's active emergency response will have important guiding significance for the analysis of the impact of human active emergency response on human injury characteristics in subsequent vehicle collision experiments.

Can we reposition finite element human body model like dummies?

Rapidly repositioning finite element human body models (FE-HBMs) with high biofidelity is an important but notorious problem in vehicle safety and injury biomechanics. We propose to reposition the FE-HBM in a dummy-like manner, i.e., through pose parameters prescribing joint configurations. Skeletons are reconfigured along the trajectories inferred from model-specific bone geometries. We leverage differential geometry to steer equidistant moves along the congruent articulated bone surfaces. Soft tissues are subsequently adapted to reconfigured skeletons through a series of operations. The morph-contact algorithm allows the joint capsule to slide and wrap around the repositioned skeletons. Nodes on the deformed capsule are redistributed following an optimization-based approach to enhance element regularity. The soft tissues are transformed accordingly via thin plate spline. The proposed toolbox can reposition the Total Human Body Model for Safety (THUMS) in a few minutes on a whole-body level. The repositioned models are simulation-ready, with mesh quality maintained on a comparable level to the baseline. Simulations of car-to-pedestrian impact with repositioned models exhibiting active collision-avoidance maneuvers are demonstrated to illustrate the efficacy of our method. This study offers an intuitive, effective, and efficient way to reposition FE-HBMs. It benefits all posture-sensitive works, e.g., out-of-position occupant safety and adaptive pedestrian protection. Pose parameters, as an intermediate representation, join our method with recently prosperous perception and reconstruction techniques of the human body. In the future, it is promising to build a high-fidelity digital twin of real-world accidents using the proposed method and investigate human biomechanics therein, which is of profound significance in reshaping transportation safety studies in the future.

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the "stop-go" state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle's active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency.

Rethinking infrastructure design: evaluating pedestrians and VRUs' psychophysiological and behavioral responses to different roadway designs

The integration of human-centric approaches has gained more attention recently due to more automated systems being introduced into our built environments (buildings, roads, vehicles, etc.), which requires a correct understanding of how humans perceive such systems and respond to them. This paper introduces an Immersive Virtual Environment-based method to evaluate the infrastructure design with psycho-physiological and behavioral responses from the vulnerable road users, especially for pedestrians. A case study of pedestrian mid-block crossings with three crossing infrastructure designs (painted crosswalk, crosswalk with flashing beacons, and a smartphone app for connected vehicles) are tested. Results from 51 participants indicate there are differences between the subjective and objective measurement. A higher subjective safety rating is reported for the flashing beacon design, while the psychophysiological and behavioral data indicate that the flashing beacon and smartphone app are similar in terms of crossing behaviors, eye tracking measurements, and heart rate. In addition, the smartphone app scenario appears to have a lower stress level as indicated by eye tracking data, although many participants do not have prior experience with it. Suggestions are made for the implementation of new technologies, which can increase public acceptance of new technologies and pedestrian safety in the future.

Kinetic and Kinematic Features of Pedestrian Avoidance Behavior in Motor Vehicle Conflicts

The active behaviors of pedestrians, such as avoidance motions, affect the resultant injury risk in vehicle–pedestrian collisions. However, the biomechanical features of these behaviors remain unquantified, leading to a gap in the development of biofidelic research tools and tailored protection for pedestrians in real-world traffic scenarios. In this study, we prompted subjects (“pedestrians”) to exhibit natural avoidance behaviors in well-controlled near-real traffic conflict scenarios using a previously developed virtual reality (VR)-based experimental platform. We quantified the pedestrian–vehicle interaction processes in the pre-crash phase and extracted the pedestrian postures immediately before collision with the vehicle; these were termed the “pre-crash postures.” We recorded the kinetic and kinematic features of the pedestrian avoidance responses—including the relative locations of the vehicle and pedestrian, pedestrian movement velocity and acceleration, pedestrian posture parameters (joint positions and angles), and pedestrian muscle activation levels—using a motion capture system and physiological signal system. The velocities in the avoidance behaviors were significantly different from those in a normal gait (p < 0.01). Based on the extracted natural reaction features of the pedestrians, this study provides data to support the analysis of pedestrian injury risk, development of biofidelic human body models (HBM), and design of advanced on-vehicle active safety systems.