Non-stationary casual structures are prevalent in real-world physical systems.
In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles.
Therefore, it is desirable for the trained model to adapt to new tasks that have limited data samples available.
The simulation results shows that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density.
The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.
A latent variable predictor is proposed to estimate latent variable distributions from observed and ground-truth trajectories.
Recent studies focus on modeling pedestrians' motion patterns with recurrent neural networks, capturing social interactions with pooling-based or graph-based methods, and handling future uncertainties by using random Gaussian noise as the latent variable.
Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving.
Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.
Lane change is a challenging task which requires delicate actions to ensure safety and comfort.
The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area.
We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver.
A reliable controller is critical and essential for the execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, and wind conditions, and so on. It also needs to deal with the internal parametric variations of vehicle sub-systems, including power-train efficiency, measurement errors, time delay, so on. Moreover, as in most production vehicles, the low-control commands for the engine, brake, and steering systems are delivered through separate electronic control units. These aforementioned factors introduce opaque and ineffectiveness issues in controller performance. In this paper, we design a feed-forward compensate process via a data-driven method to model and further optimize the controller performance. We apply the principal component analysis to the extraction of most influential features. Subsequently, we adopt a time delay neural network and include the accuracy of the predicted error in a future time horizon. Utilizing the predicted error, we then design a feed-forward compensate process to improve the control performance. Finally, we demonstrate the effectiveness of the proposed feed-forward compensate process in simulation scenarios.
Most importantly, in contrast to most reinforcement learning applications in which the action space is resolved as discrete, our approach treats the action space as well as the state space as continuous without incurring additional computational costs.
To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment.