Designing Human-machine Collaboration Interface Through Multimodal Combination Optimization to Improve Takeover Performance in Highly Automated Driving.

Drivers in highly automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over vehicles when requested. Therefore, it is necessary to design an in-cabin interface that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study examined the interaction effects of (1) takeover request modality (TORM), (2) non-driving related tasks (NDRT), (3) takeover lead time (TOLT) on takeover performance. We conducted a driver-in-the-loop experiment involving 32 participants based on 15 takeover requests (TORs) for each NDRT. The Particle swarm optimization algorithm combined with multilayer perceptron learning was used for multi-objective balance optimization of five performance indicators. Results showed that the utilization of tactile-auditory prompts with 5 s and 9 s TOLTs exhibited positive performance in music listening and no task situations. The integration of tactile-auditory or tactile-visual cues with 7 s and 9 s TOLTs yielded favorable results in reading scenarios, whereas the tactile cues demonstrated efficacy in video watching scenarios. The situation of visual with 5 s TOLT reached the optimal balance of the five optimization objectives when the driver performed no task. This finding can offer valuable guidance to design interfaces in highly automated vehicles.