Collision-Probability-Aware Human-Machine Cooperative Planning for Safe Automated Driving

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-pRRT, 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-pRRT 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.

Automated summary

This paper investigates a novel collision probability-aware human-machine cooperative planning and tracking method to enhance the safety of automated vehicles. The proposed method effectively mitigates collisions and ensures safe trajectories by incorporating long-term trajectory prediction and risk assessment systems. Test results demonstrate the method's capability to reduce conflicts and enhance vehicle safety during human-machine interactions.