TY - GEN
T1 - Cost-Based Non-deep Learning for Planning and Decision-Making Towards Enhancing Autonomous Driving’s Safety and Comfort
AU - Gan, Shihao
AU - Wu, Siyao
AU - Luo, Yang
AU - Zhang, Fan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - In pursuit of enhancing driving safety while controlling and reducing vehicle and testing costs, our research explores advanced decision logic for autonomous driving. This study integrates foundational algorithms and employs a suite of tools, including MATLAB, Simulink, Prescan, and CarSim, to develop a comprehensive virtual simulation testing environment. Within this virtual platform, we create and test various environmental parameters and vehicle states to simulate real-world driving conditions accurately. Our core cost-based decision-making algorithm processes perceptual results to calculate the “cost,” which then informs driving decisions and planning. By default, the planning tried not to decelerate, but maintained acceleration changes as smooth as possible, which leading to unsafe scenarios such as driving off-road to go around the obstacles. In contrast, we proposed an explainable algorithm that could effectively balances safety and comfort, ensuring safer driving outcomes. Furthermore, our work could contribute to the field of Explainable Artificial Intelligence in Autonomous Driving by providing a robust framework for testing and optimizing decision logic, ultimately leading to the development of safe and efficient autonomous vehicles.
AB - In pursuit of enhancing driving safety while controlling and reducing vehicle and testing costs, our research explores advanced decision logic for autonomous driving. This study integrates foundational algorithms and employs a suite of tools, including MATLAB, Simulink, Prescan, and CarSim, to develop a comprehensive virtual simulation testing environment. Within this virtual platform, we create and test various environmental parameters and vehicle states to simulate real-world driving conditions accurately. Our core cost-based decision-making algorithm processes perceptual results to calculate the “cost,” which then informs driving decisions and planning. By default, the planning tried not to decelerate, but maintained acceleration changes as smooth as possible, which leading to unsafe scenarios such as driving off-road to go around the obstacles. In contrast, we proposed an explainable algorithm that could effectively balances safety and comfort, ensuring safer driving outcomes. Furthermore, our work could contribute to the field of Explainable Artificial Intelligence in Autonomous Driving by providing a robust framework for testing and optimizing decision logic, ultimately leading to the development of safe and efficient autonomous vehicles.
KW - Autonomous Driving
KW - Decision-making
KW - Dynamic Planning
KW - Explainable Artificial Intelligence
KW - Safety-comfort Trade-off
UR - http://www.scopus.com/inward/record.url?scp=105002715588&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_18
DO - 10.1007/978-981-96-3949-6_18
M3 - Conference Proceeding
AN - SCOPUS:105002715588
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 234
EP - 252
BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
A2 - Chen, Wei
A2 - Ping Tan, Andrew Huey
A2 - Luo, Yang
A2 - Huang, Long
A2 - Zhu, Yuyi
A2 - PP Abdul Majeed, Anwar
A2 - Zhang, Fan
A2 - Yan, Yuyao
A2 - Liu, Chenguang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
Y2 - 22 August 2024 through 23 August 2024
ER -