Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles
This work addresses the comfort and personalization needs for users of autonomous vehicles, though it appears incremental as it builds on existing control methods with a focus on preference integration.
The paper tackled the problem of making autonomous vehicles drive more like human drivers by personalizing the driving experience based on occupant preferences, resulting in a system that successfully tracked specified acceleration and jerk criteria in simulations and experiments.
This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose an Occupant's Preference Metric (OPM) which is defining a preferred lateral and longitudinal acceleration region with maximum allowable jerk for users. Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles. The proposed system not only provides the criteria for the occupant's driving preference, but also provides a personalized autonomous self-driving style like a human driver instead of a Robocar. The simulation and experimental results demonstrated that the proposed system can maneuver the self-driving vehicle like a human driver by tracking the specified criterion of admissible acceleration and jerk.