English

Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles

Systems and Control 2022-11-21 v2 Robotics Systems and Control

Abstract

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.

Keywords

Cite

@article{arxiv.2001.03908,
  title  = {Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles},
  author = {Il Bae and Jaeyoung Moon and Junekyo Jhung and Ho Suk and Taewoo Kim and Hyungbin Park and Jaekwang Cha and Jinhyuk Kim and Dohyun Kim and Shiho Kim},
  journal= {arXiv preprint arXiv:2001.03908},
  year   = {2022}
}

Comments

8 pages, 9 figures, NeurIPS 2019 Workshop: Machine Learning for Autonomous Driving (ML4AD)