Collision-Probability-Aware Human-Machine Cooperative Planning for Safe Automated Driving
- 10 August 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Vehicular Technology
- Vol. 70 (10) , 9752-9763
Abstract
In this paper, we investigate a novel collision probability-aware human-machine cooperative planning and tracking method for enhancing safety of automated vehicles. Firstly, a long-term trajectory prediction is obtained by using Gaussian mixture models with vehicle historical data. After that, a novel risk assessment system based on the dynamic potential field (DPF) and the fuzzy inference system (FIS) is proposed to evaluate the risk level of the human driver's behaviors. Based on the assessed human driving risk, the human-machine cooperation is activated adaptively during the trajectory planning. A novel human-machine cooperative trajectory planning algorithm, named as HM- $p$ RRT, is proposed and used to incorporate the driver's intent and automation's corrective actions during trajectory planning. Testing results show that the proposed HM- $p$ RRT algorithm is able to simultaneously mitigate collision and provide a safe trajectory, effectively ensuring the safety of the vehicle and reducing conflicts during human-machine interactions.Keywords
Funding Information
- A*STAR National Robotics Programme (SERC 1922500046)
- SUG-NAP (M4082268.050)
- Nanyang Technological University
This publication has 32 references indexed in Scilit:
- Spline-Based Motion Planning for Autonomous Guided Vehicles in a Dynamic EnvironmentIEEE Transactions on Control Systems Technology, 2017
- An Investigation of Emotion Prediction Uncertainty Using Gaussian Mixture Regression2017
- Risk Level Assessment for Rear-End Collision with Bayesian NetworkIFAC-PapersOnLine, 2017
- A Potential Field Approach-Based Trajectory Control for Autonomous Electric Vehicles With In-Wheel MotorsIEEE Transactions on Intelligent Transportation Systems, 2016
- Kinematic and dynamic vehicle models for autonomous driving control design2015
- Trajectory planning for car-like robots in unknown, unstructured environments2014
- Continuous tool wear prediction based on Gaussian mixture regression modelThe International Journal of Advanced Manufacturing Technology, 2012
- A unified framework of the automated lane centering/changing control for motion smoothness adaptation2012
- Probabilistic trajectory prediction with Gaussian mixture models2012
- Curvature continuous path generation for autonomous vehicle using B-spline curvesComputer-Aided Design, 2010