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 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 …

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

Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore,
it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most …

Autonomous driving decision-making is a challenging task due to the inherent complexity
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …

Driving in the complex traffic safely and efficiently is a difficult task for autonomous vehicle
because of the stochastic characteristics of engaged human drivers. Deep reinforcement …

In this study, a deep reinforcement learning approach is proposed to handle tactical driving
in complex highway traffic environments for unmanned ground vehicles. Tactical driving is a …

This work optimizes the highway decision making strategy of autonomous vehicles by using
deep reinforcement learning (DRL). First, the highway driving environment is built, wherein …

Driving through complex urban environments is a challenging task for autonomous vehicles
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …

Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle's
automatic speed control strategy to make judgments and effective control decisions. In this …