Deep Q-Network Based Decision Making for Autonomous Driving

• URL: http://arxiv.org/abs/2303.11634v1
• Date: Tue, 21 Mar 2023 07:01:22 GMT
• Title: Deep Q-Network Based Decision Making for Autonomous Driving
• Authors: Max Peter Ronecker, Yuan Zhu
• Abstract summary: This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner. The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers.
• Score: 1.0152838128195467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner. The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers. In order to prove the functionality of this approach it is evaluated on two different highway traffic scenarios. Furthermore, the impact of different state representations on the performance and training process is analyzed. The results show that the proposed system can produce efficient and safe driving behavior.

Related papers

• Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
  We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
  arXiv  Detail & Related papers  (2024-07-09T04:56:11Z)
• DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving [81.04174379726251]
  This paper collects a comprehensive end-to-end driving dataset named DriveCoT. It contains sensor data, control decisions, and chain-of-thought labels to indicate the reasoning process. We propose a baseline model called DriveCoT-Agent, trained on our dataset, to generate chain-of-thought predictions and final decisions.
  arXiv  Detail & Related papers  (2024-03-25T17:59:01Z)
• Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors [7.812717451846781]
  We consider a behavioral model that incorporates both social behaviors and personal objectives of the interacting drivers. We develop a receding-horizon control-based decision-making strategy that estimates online the other drivers' intentions.
  arXiv  Detail & Related papers  (2023-09-25T19:49:14Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving [1.995792341399967]
  We develop a safe decision-making method for self-driving cars in a multi-lane, single-agent setting. The proposed approach utilizes deep reinforcement learning to achieve a high-level policy for safe tactical decision-making.
  arXiv  Detail & Related papers  (2021-05-13T19:17:30Z)
• A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge [49.976633450740145]
  This paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge.
  arXiv  Detail & Related papers  (2020-10-23T18:07:48Z)
• Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon [14.059728921828938]
  This paper utilizes the deep reinforcement learning (DRL) method to address the continuous-horizon decision-making problem on the highway. The running objective of the ego automated vehicle is to execute an efficient and smooth policy without collision. The PPO-DRL-based decision-making strategy is estimated from multiple perspectives, including the optimality, learning efficiency, and adaptability.
  arXiv  Detail & Related papers  (2020-08-26T22:49:27Z)
• Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning [6.298084785377199]
  A deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions. The DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely.
  arXiv  Detail & Related papers  (2020-07-16T23:41:48Z)
• Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study [9.602219035367066]
  This work optimize the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL) The overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.
  arXiv  Detail & Related papers  (2020-07-16T14:09:20Z)
• Intelligent Roundabout Insertion using Deep Reinforcement Learning [68.8204255655161]
  We present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver.
  arXiv  Detail & Related papers  (2020-01-03T11:16:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.