Human-Machine Cooperative Trajectory Planning and Tracking for Safe Automated Driving

Abstract

This paper investigates a human-machine cooperative trajectory planning and tracking control approach for automated vehicles. The proposed method is developed based on a novel algorithm of cooperative human-machine rapidly-exploring random (HM-RRT) for path planning, together with the risk assessment of driver behavior. First, the driver's behaviour is assessed according to the information of the predicted vehicle trajectory, the identified safe driving area and the driving risks evaluated in both lateral and longitudinal directions. Based on the driver's expected driving task, when driving risks are identified by real-time assessment, then the human-machine cooperation is activated during trajectory planning. By HM-RRT, the newly developed safety assurance mechanism for path planning, the cooperative trajectory is then generated, which incorporates the driver's desire and actions and automation's corrective actions, to ensure the safety, stability and smoothness of the human-vehicle system. The simulation and experimental results show that the proposed HM-RRT algorithm can effectively improve the convergence rate and reduce the computation load, comparing to the conventional method. Beyond this, the proposed human-machine cooperation approach is able to simultaneously ensure the safety, stability and smoothness of the vehicle and largely reduce human-machine conflicts in real-time applications, demonstrating its feasibility and effectiveness.

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

“…[272] showed a personalized cooperative control system which could automatically adjust the related parameters based on the individual driving pattern, to make the assistance system easier to accept. [273,274] proposed a collision probability-aware human-machine cooperative planning and tracking method by evaluating the risk level of the human driver's behavior; the system could be adaptively activated to incorporate the driver's intention and improve the automated vehicle's safety. [275] presented a human-machine adaptive shared control method to tackle automation performance degradation, wherein the control authority can be adaptively allocated by monitoring the driver state and automated vehicle performance.…”

Human–Machine Telecollaboration Accelerates the Safe Deployment of Large-Scale Autonomous Robots During the COVID-19 Pandemic

“…Through multimodal interfaces, including image processing, gesture recognition, natural language processing, and affective computing, the operator’s intentions can be inferred and passed to the machines to enhance their reasoning ( Yang et al, 2018 ). Recently, we proposed a novel human–machine cooperative rapidly exploring random tree algorithm, which introduces human preferences and corrective actions, enhancing the safety, smoothness, and human likeness of robot planning and tracking control ( Huang et al, 2021 ). In addition, to further improve machine intelligence, we are developing a human-in-the-loop deep reinforcement learning (DRL) method by fusing human skills via real-time guidance into the DRL agent during the learning process, which effectively improves the learning curve ( Wu et al, 2021 ).…”

QPNet: Lane‐changing trajectory planning combining quadratic programming and neural network under the convex optimization framework

“…There are two planning technologies using this kind of planner: rapidly-exploring random tree (RRT) and state lattice. Huang et al [21] presented a humanmachine cooperative trajectory planning and tracking control approach. The proposed method is developed based on the human-machine RRT algorithm for path planning, together with the risk assessment of driver behaviour.…”

“…Remarks: Modelling lane-changing behaviour and designing lane-changing trajectory planning algorithm for ADAS are two This method can find a collision-avoidance path quickly and is suitable for global searching; the curvature of the generated path is not continuous; the method depends on the nearest neighbour heuristic algorithm. [21,22] State lattice Uses the same grid to divide the search space offline, and then employs the appropriate heuristic search algorithms (such as A* search algorithm, ant colony algorithm etc.) to generate the optimal trajectory.…”

“…The reason for the linear car-following application is that the convex optimization framework we propose requires that the constraints should be linear, while the nonlinear car-following model is more reflective of real-world driving behaviour. In this linear car-following model, if vehicle n is behind vehicle n + 1, the car-following acceleration of vehicle n is given as Equation (21),…”