Autonomous Driving Learning Preference of Collision Avoidance Maneuvers

Recently, with the active development of automated driving systems (ADSs) corresponding to SAE automated driving level 2, the comfort of ADSs has gained significant attention. In our previous research, it was proposed that the comfort of ADSs is affected by their maneuvers and the degree of information sharing between ADS and drivers. However, even if the drivers are well-informed of the recognized traffic environment and ADS-controlled trajectory, the comfort and trust of drivers in ADSs could be insufficient. This is because each driver has distinct preferred trajectories. Although some researchers have proposed ADSs that learn driver preferences, the maneuvers presented by these systems are not easily modified, because of off-line learning. In addition, a few quantitative investigations on the subjective evaluation of comfort have been conducted. This study proposes an on-demand learning collision avoidance ADS that learns the preferred maneuvers of drivers through driver intervention. In our system, the drivers teach their preferred trajectory to the system only if they are unsatisfied with the maneuver presented by the system. The system updates the parameters and shows the learned maneuver at the next avoidance. To realize the proposed system, we applied modified risk potential functions and gain-tuning method, as well as a cost function and learning method for gradual and stable maneuver learning. Driving simulator experiments demonstrated stable trajectory learning and smooth intervention. Furthermore, the drivers were satisfied with the learned maneuvers of the ADS, and their comfort and trust in the ADS improved when using the proposed ADS.