Impact of information-assisted modalities and levels on driver takeover performance in conditional automated driving

With advances in autonomous driving technology, conditional automated driving (CAD) is gradually entering the consumer market. Before full automation is achieved, however, drivers are still required to take control of the vehicle in complex scenarios. To increase safety and comfort in CAD systems, drivers must be able to respond promptly to takeover requests (TORs). While previous studies have focused on warning signals, it remains unclear how to provide supportive information for complex operations during TORs to optimize post-takeover behavior. This study designed and evaluated a multimodal in-vehicle information assistance system to support safer driver takeovers by providing real-time road information, such as hazard warnings and lane availability. The study introduced two independent variables, three information assistance modalities (visual (V), auditory (A), and visual + auditory (V + A)) and two levels of assistance information (hazard information (H) and hazard information + operational suggestions (HO)). A driving simulator experiment with 56 participants assessed the effects of these conditions on driving performance, gaze behavior, and subjective evaluation. Results demonstrated that the V + A modality combined with HO information achieved the best performance in takeover speed and lane-change safety. Compared to the baseline, takeover time was reduced by 0.46 s, and time-to-collision (TTC) improved by 3.59 s, reducing collision risk. The visual (V) modality enhanced vehicle stability, lowering maximum resultant acceleration by 0.73 m/s² and reducing abrupt maneuvers. The HO condition improved decision-making quality, increasing overtaking success rates by 6.25 % without adding cognitive load. Additionally, the V + A and HO combination optimized attention allocation. Multisensory integration (V + A) enhanced environmental awareness and decision speed by minimizing information omission through simultaneous visual and auditory cues. These findings provide key insights into designing information assistance for future automated driving, emphasizing the importance of optimizing information delivery in emergencies to improve safety and user experience.