Overview of Decision Making System For Lane Change Using Deep Reinforcement
Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes , receives the state information of the host vehicle and a remote vehicle that are both equipped with vehicular ...
In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication.
PDF Decision
Abstract: Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes , receives the state information of the host vehicle and a remote vehicle that are both equipped with ...
Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving Electronics, 8 (5) (2019), p. 543, 10.3390/electronics8050543
Lane Change Decision
Drivers usually make better decisions than the driving system in complex driving situations at the current level of autonomous driving. Combined with the driver decision-making advantage, a new Deep Reinforcement Learning architecture named dc-DRL is proposed for the lane change decision-making tasks in this study. Through the input of external drivers' control actions in experience replay ...
Personalized Decision-Making Framework for Collaborative Lane Change and Speed Control Based on Deep Reinforcement Learning Abstract: Autonomous driving (AD) is critically dependent on intelligent decision-making technology, which is the crucial ingredient in driving safety and overall vehicle performance.
Decision making of autonomous vehicles in lane change scenarios
Abstract Driving safety is the most important element that needs to be considered for autonomous vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making framework based on deep reinforcement learning to find a risk-aware driving decision strategy with the minimum expected risk for autonomous driving.
A safe and efficient lane change behavior may be obtained by combining high-level lateral decision-making with low-level rule-based trajectory monitoring. The agent is anticipated to perform appropriate lane-change maneuvers in a real-world-like udacity simulator after training it for a total of 100 episodes.
A Hybrid Input based Deep Reinforcement Learning for Lane Change ...
Currently, lane -changing decision models include two common methods: a rule-based decision algorithm and a reinforcement learning decision algorithm. The rule-based decision algorithm defines the behavior mode of the vehicle in different scenarios and uses characteristic variables as the basis for judgment when the driving condition switches.
