Robustness and adaptability of reinforcement learning-based cooperative autonomous driving in mixed-autonomy traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
Integrated decision and control: Toward interpretable and computationally efficient driving intelligence
Decision and control are core functionalities of high-level automated vehicles. Current
mainstream methods, such as functional decomposition and end-to-end reinforcement …
mainstream methods, such as functional decomposition and end-to-end reinforcement …
Resilient branching MPC for multi-vehicle traffic scenarios using adversarial disturbance sequences
An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic
scenarios is proposed. The proposed method is based on model predictive control (MPC) …
scenarios is proposed. The proposed method is based on model predictive control (MPC) …
Hybrid autonomous driving guidance strategy combining deep reinforcement learning and expert system
The complex traffic and road environment pose considerable challenges to the accuracy,
timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making …
timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making …
TD3LVSL: A lane-level variable speed limit approach based on twin delayed deep deterministic policy gradient in a connected automated vehicle environment
Variable speed limit (VSL) control plays a vital role in the emerging connected automated
vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by …
vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by …
Distributed multiagent coordinated learning for autonomous driving in highways based on dynamic coordination graphs
C Yu, X Wang, X Xu, M Zhang, H Ge… - Ieee transactions on …, 2019 - ieeexplore.ieee.org
Autonomous driving is one of the most important AI applications and has attracted extensive
interest in recent years. A large number of studies have successfully applied reinforcement …
interest in recent years. A large number of studies have successfully applied reinforcement …
Socially-attentive policy optimization in multi-agent self-driving system
As increasing numbers of autonomous vehicles (AVs) are being deployed, it is important to
construct a multi-agent self-driving (MASD) system for navigating traffic flows of AVs. In an …
construct a multi-agent self-driving (MASD) system for navigating traffic flows of AVs. In an …
Integrating deep reinforcement learning with model-based path planners for automated driving
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
Deep predictive autonomous driving using multi-agent joint trajectory prediction and traffic rules
Autonomous driving is a challenging problem because the autonomous vehicle must
understand complex and dynamic environment. This understanding consists of predicting …
understand complex and dynamic environment. This understanding consists of predicting …
Robust lane change decision making for autonomous vehicles: An observation adversarial reinforcement learning approach
Reinforcementlearning holds the promise of allowing autonomous vehicles to learn complex
decision making behaviors through interacting with other traffic participants. However, many …
decision making behaviors through interacting with other traffic participants. However, many …