Abstract
The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h).
Highlights
Autonomous driving is an active research topic that has attracted considerable attention from both academic institutions and manufacturing companies, owing to its broad application prospects in intelligent transportation systems
PID control based on reinforcement learning (RL); we present a newly developed approach for training an actor–critic network policy on a simulator and transferring it to a state-of-the-art realistic vehicle
We propose a self-optimized PID controller with a new adaptive updating rule, based on a reinforcement learning framework for autonomous vehicle path tracking control systems, in order to track a predefined path with high accuracy and, simultaneously, provide a comfortable riding experience
Summary
Autonomous driving is an active research topic that has attracted considerable attention from both academic institutions and manufacturing companies, owing to its broad application prospects in intelligent transportation systems. Automated vehicle software mainly involve environmental perception, decision planning, and motion control. Intelligent vehicles are non-linear motion systems, and their dynamic parameters change significantly with different speeds and road conditions, especially at high speeds of motion and during complex trajectories. This makes the path tracking control problem one of the most challenging aspects of this field. A closed-loop control system, which is composed of people, vehicles, and roads, as shown, is influenced by inevitable disturbances 4.0/). A closed-loop control system, which is composed of people, vehicles, and roads, as shown in Figure 1, is influenced by inevitable disturbances 4.0/).
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