To improve the safety and efficiency of autonomous vehicles on the highway, a hierarchical
framework combining deep reinforcement learning and risk assessment is proposed in this …

For high levels autonomous driving functions, the Decision Layer often takes on more
responsibility due to the requirement of facing more diverse and even rare conditions. It is …

Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …

Uncertain environment on multi-lane highway, eg, the stochastic lane-change maneuver of
surrounding vehicles, is a big challenge for achieving safe automated highway driving. To …

To fulfill high-level automation, an automated vehicle needs to learn to make decisions and
control its movement under complex scenarios. Due to the uncertainty and complexity of the …

Great progress has been made in the field of machine learning in recent years. And learning-
based methods have been widely utilized for developing highly autonomous vehicle. To this …

Autonomous driving decision-making is a great challenge due to the complexity and
uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q …

Due to the complexity and volatility of the traffic environment, decision-making in
autonomous driving is a significantly hard problem. In this project, we use a Deep Q …

To further enhance decision making in autonomous vehicles field, grant more safety,
comfort, reduce traffic, and accidents, learning approaches were adopted, mainly …

Aiming at poor effects and single consideration factors of traditional driving decision-making
algorithm in high-speed and complex environment, a method based on improved deep …