Shaping driver-vehicle interaction in autonomous vehicles: How the new in-vehicle systems match the human needs

A matched case-control analysis of autonomous vs human-driven vehicle accidents

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles' levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous' versus Human-Driven Vehicles' accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.

Exploring the Timing of Disengagement From Nondriving Related Tasks in Scheduled Takeovers With Pre-Alerts: An Analysis of Takeover-Related Measures

OBJECTIVES

This study aimed to investigate drivers' disengagement from nondriving related tasks (NDRT) during scheduled takeovers and to evaluate its impact on takeover performance.

BACKGROUND

During scheduled takeovers, drivers typically have sufficient time to prepare. However, inadequate disengagement from NDRTs can introduce safety risks.

METHOD

Participants experienced scheduled takeovers using a driving simulator, undergoing two conditions, with and without an NDRT. We assessed their takeover performance and monitored their NDRT disengagement from visual, cognitive, and physical perspectives.

RESULTS

The study examined three NDRT disengagement timings (DTs): DT1 (disengaged before the takeover request), DT2 (disengaged after the request but before taking over), and DT3 (not disengaged). The impact of NDRT on takeover performance varied depending on DTs. Specifically, DT1 demonstrated no adverse effects; DT2 impaired takeover time, while DT3 impaired both takeover time and quality. Additionally, participants who displayed DT1 exhibited longer eye-off-NDRT duration and a higher eye-off-NDRT count during the prewarning stage compared to those with DT2 and DT3.

CONCLUSION

Drivers can benefit from earlier disengagement from NDRTs, demonstrating resilience to the adverse effects of NDRTs on takeover performance. The disengagement of cognition is often delayed compared to that of eyes and hands, potentially leading to DT3. Moreover, visual disengagement from NDRTs during the prewarning stage could distinguish DT1 from the other two.

APPLICATION

Our study emphasizes considering NDRT disengagement in designing systems for scheduled takeovers. Measures should be taken to promote early disengagement, facilitate cognitive disengagement, and employ visual disengagement during the prewarning period as predictive indicators of DTs.

Effect of hip flexion angle on lower limb injuries of occupants in autonomous vehicle crashes

This study aims to determine the influence of the hip flexion angle on the injury trends of lower limbs. An impact model was established using a hybrid human body model and an accurate vehicle model. Simulations were performed in two boundary environments of 25 and 40% overlap impacts under different hip flexion angles. The analysis of the dynamic responses indicated that the hip flexion angle significantly affected the injury trends. The maximum femur index of different overlaps was all found at the minimum hip angle, except for the left femur at 25% overlap rate. Meanwhile, the maximum acetabular stress was all found at the minimum hip angle (approximately 0.09-0.20 GPa). This study provides mechanistic insights into the lower limb injuries associated with complex human postures.

A new method to identifying optimal adjustment strategy when the car cockpit is uncomfortable: optimal state distance method

With the rapid development of the automobile industry, the comfort of the cockpit has become the standard for judging the quality of the car. People have also put forward higher requirements for cockpit comfort. In the process of driving, the cockpit environment will constantly change, and the comfort will also change. When the comprehensive comfort level of the cockpit decreases and the occupants feel uncomfortable, the cockpit comfort should be adjusted. In this article, a cockpit comfort evaluation model is established to realize the evaluation of cockpit comfort. In addition, we elaborate the theory of optimal state distance, where the numerical magnitude of the optimal state distance is used to reflect the extent to which an indicator deviates from its optimal state. Also, a cockpit optimal adjustment strategy identification model is established based on the theory, which can obtain the optimal adjustment strategy in a certain cockpit operating environment, facilitate the timely adjustment of the corresponding actuator, and realize the dynamic monitoring and adjustment of cockpit comfort. This project provides a reference direction for cockpit comfort adjustment, which is of great significance for future research and development of automotive cockpit comfort.

How does eco-driving make us feel? Considering the psychological effects of eco-driving

Despite both the environmental and financial benefits of eco-driving being well known, the psychological impact of engaging in eco-driving behaviours has received less attention within the literature. It was anticipated that being asked to engage in eco-driving behaviours not only has an impact on vehicle fuel usage, but also on the driver, both in terms of their overall mood and willingness to re-engage with the task at a later time. Results from a simulated driving study suggest that although eco-driving was beneficial in reducing fuel consumption, being asked to eco-drive had a negative effect on overall journey time and mood. Engaging in eco-driving did however have a positive effect on self-esteem, suggesting potential longer term psychological benefits of adopting this behaviour.

Would You Trust Driverless Service? Formation of Pedestrian's Trust and Attitude Using Non-Verbal Social Cues

Despite the widespread application of Autonomous Vehicles (AV) to various services, there has been relatively little research carried out on pedestrian–AV interaction and trust within the context of service provided by AV. This study explores the communication design strategy promoting a pedestrian’s trust and positive attitude to driverless services within the context of pedestrian–AV interaction using non-verbal social cues. An empirical study was conducted with an experimental VR environment to measure participants’ intimacy, trust, and brand attitude toward AV. Further understanding of their social interaction experiences was explored through semi-structured interviews. As a result of the study, the interaction effect of social cues was found, and it was revealed that brand attitude was formed by the direct effects of intimacy and trust as well as the indirect effects of intimacy through trust’s mediation. Furthermore, ‘Conceptual Definition of Space’ was identified to generate differences in the interplay among intimacy, trust, and brand attitude according to social cues. Quantitative and qualitative results were synthesized to discuss implications considering the service context. Practical implications were also addressed suggesting specific design strategies for utilizing the sociality of AV.

Volumetric Accuracy Analysis of Virtual Safety Barriers for Cooperative-Control Robotic Mastoidectomy

HYPOTHESIS

Virtual fixtures can be enforced in cooperative-control robotic mastoidectomies with submillimeter accuracy.

BACKGROUND

Otologic procedures are well-suited for robotic assistance due to consistent osseous landmarks. We have previously demonstrated the feasibility of cooperative-control robots (CCRs) for mastoidectomy. CCRs manipulate instruments simultaneously with the surgeon, allowing the surgeon to control instruments with robotic augmentation of motion. CCRs can also enforce virtual fixtures, which are safety barriers that prevent motion into undesired locations. Previous studies have validated the ability of CCRs to allow a novice surgeon to safely complete a cortical mastoidectomy. This study provides objective accuracy data for CCR-imposed safety barriers in cortical mastoidectomies.

METHODS

Temporal bone phantoms were registered to a CCR using preoperative computed tomography (CT) imaging. Virtual fixtures were created using 3D Slicer, with 2D planes placed along the external auditory canal, tegmen, and sigmoid, converging on the antrum. Five mastoidectomies were performed by a novice surgeon, moving the drill to the limit of the barriers. Postoperative CT scans were obtained, and Dice coefficients and Hausdorff distances were calculated.

RESULTS

The average modified Hausdorff distance between drilled bone and the preplanned volume was 0.351 ± 0.093 mm. Compared with the preplanned volume of 0.947 cm3, the mean volume of bone removed was 1.045 cm3 (difference of 0.0982 cm3 or 10.36%), with an average Dice coefficient of 0.741 (range, 0.665-0.802).

CONCLUSIONS

CCR virtual fixtures can be enforced with a high degree of accuracy. Future studies will focus on improving accuracy and developing 3D fixtures around relevant surgical anatomy.

Pedestrian assessment: Is displaying automated driving mode in self-driving vehicles as relevant as emitting an engine sound in electric vehicles?

Pedestrians rely on vehicle dynamics, engine sound, and driver cues. The lack of engine sound now constitutes an addressed pedestrian safety issue for (hybrid) electric vehicles ((H)EVs). Analogously, lacking driver cues may constitute a pedestrian safety issue for self-driving vehicles (SDVs). The purpose of this study was to systematically compare the relevance of substituting driver cues with an external human-machine interface among SDVs (no eHMI vs. eHMI) with the relevance of substituting engine sound with artificial sound among (H)EVs (no engine sound vs. engine sound). In a within-subject design, twenty-nine participants acting as pedestrians encountered a simulated SDV in a parking lot. The results revealed that both informational cues have equally large effects on subjective measures such as perceived safety. In semi-structured interviews, participants stated that it is equally crucial to equip SDVs with an eHMI as equipping (H)EVs with an artificial sound generator. We conclude that an eHMI for SDVs seems to be as relevant as an artificial sound for (H)EVs.