How does training effect users’ attitudes and skills needed for highly automated driving?

Abstract

Highly automated vehicles are expected to increase the safety and quality of road transportation, but because of significant changes in the driver’s role, they introduce new human factors challenges and require learning specific skills. Training has been proposed as a potential tactic to alleviate these challenges and improve driver interaction with automated cars. This study evaluated the effects of Simulator Training and Video Training on procedural and higher-order cognitive skills required for Conditionally Automated Driving (CAD), and users’ attitudes as well. Fifty-four people participated in the experiment, 18 in Simulator Training, 18 in Video Training, and 18 in Control groups. Takeover Time (TOT), speed, speed variance, Standard Deviation of Lateral Position (SDLP), Takeover Decision Accuracy (TODA), trust, and acceptance data were collected before and after training sessions. The results showed that both training methods improved TOT, speed variance, and SDLP. Moreover, the participants in Simulator Training outperformed in deciding whether takeover was necessary or not. The results also indicated that self-reported trust was less erratic in Post-Training Driving Assessment in both Simulator and Video Training. These findings imply that training, especially where interactive learning is provided, helped the participants develop a more developed mental model of CAD and better-calibrated trust. Training programs, however, did not create meaningful changes in the number of crashes, speed, nor automation acceptability. Further research is needed to investigate learning transferability to highly automated driving on the real road.