TY - GEN
T1 - Risk Assessment on Off-road Environment from the Driver's Perspective
AU - Xie, Yujia
AU - Nie, Shida
AU - Zhang, Fawang
AU - Guo, Congshuai
AU - Guo, Lingxiong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Reasonable risk assessment is pivotal in aiding path planning algorithms to ascertain safe, collision-free paths while minimizing the path length. In this paper, we propose a unified risk field as a method of risk assessment from the driver's perspective. It synthesizes and quantifies the relationship between environmental factors, ego vehicle characteristics, and the occupants' attributes, which include passengers and cargo, enabling a comprehensive evaluation of risks. A notable feature of our method is the integration of Non-Uniform Safety Margin Expression (NSME) into the unified risk field, enabling adaptive and anisotropic safety margins. This enhancement significantly improves vehicle driving efficiency. Additionally, by incorporating driving style constraints, our method provides a more personalized risk assessment. Moreover, our approach considers terrain-related risks to minimize path slope and enhance safety. To validate the effectiveness of our method, we integrate it with the Probabilistic Roadmap (PRM) path planning algorithm and conduct thorough simulations.
AB - Reasonable risk assessment is pivotal in aiding path planning algorithms to ascertain safe, collision-free paths while minimizing the path length. In this paper, we propose a unified risk field as a method of risk assessment from the driver's perspective. It synthesizes and quantifies the relationship between environmental factors, ego vehicle characteristics, and the occupants' attributes, which include passengers and cargo, enabling a comprehensive evaluation of risks. A notable feature of our method is the integration of Non-Uniform Safety Margin Expression (NSME) into the unified risk field, enabling adaptive and anisotropic safety margins. This enhancement significantly improves vehicle driving efficiency. Additionally, by incorporating driving style constraints, our method provides a more personalized risk assessment. Moreover, our approach considers terrain-related risks to minimize path slope and enhance safety. To validate the effectiveness of our method, we integrate it with the Probabilistic Roadmap (PRM) path planning algorithm and conduct thorough simulations.
UR - https://www.scopus.com/pages/publications/105001672912
U2 - 10.1109/ITSC58415.2024.10920112
DO - 10.1109/ITSC58415.2024.10920112
M3 - Conference contribution
AN - SCOPUS:105001672912
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2907
EP - 2913
BT - 2024 IEEE 27th International Conference on Intelligent Transportation Systems, ITSC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024
Y2 - 24 September 2024 through 27 September 2024
ER -