SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

• URL: http://arxiv.org/abs/2211.10871v1
• Date: Sun, 20 Nov 2022 05:09:12 GMT
• Title: SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
• Authors: Wenlu Du, Junyi Ye, Jingyi Gu, Jing Li, Hua Wei, Guiling Wang
• Abstract summary: One-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing. We propose a safety-enhanced residual reinforcement learning method (SafeLight) Our method can significantly reduce collisions while increasing traffic mobility.
• Score: 5.862792724739738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.

Related papers

• A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts [17.434924472015812]
  This paper introduces a learning-based algorithm tailored to foster safe and efficient driving behaviors in roundabouts. The proposed algorithm employs a deep Q-learning network to learn safe and efficient driving strategies in complex multi-vehicle roundabouts. The results show that our proposed system consistently achieves safe and efficient driving whilst maintaining a stable training process.
  arXiv  Detail & Related papers  (2024-08-15T16:10:25Z)
• Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach [2.681732331705502]
  The study employs a Deep Q Network (DQN) to regulate traffic signals and driving behaviors of both CAVs and Human Drive Vehicles (HDVs) The findings demonstrate a significant reduction in rear-end and crossing conflicts through the combined implementation of CAVs and DQNs-based traffic signal control.
  arXiv  Detail & Related papers  (2024-05-29T16:17:19Z)
• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [54.60865656161679]
  Adversarial Training (CAT) is a framework for safe end-to-end driving in autonomous vehicles. Cat aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios. Cat can effectively generate adversarial scenarios countering the agent being trained.
  arXiv  Detail & Related papers  (2023-10-19T02:49:31Z)
• Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning [62.997667081978825]
  We compare two learnable navigation policies: safe and unsafe. The safe policy takes the constraints into the account, while the other does not. We show that the safe policy is able to generate trajectories with more clearance (distance to the obstacles) and makes less collisions while training without sacrificing the overall performance.
  arXiv  Detail & Related papers  (2023-07-27T01:04:57Z)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• Adaptive Aggregation for Safety-Critical Control [3.1692938090731584]
  We propose an adaptive aggregation framework for safety-critical control. Our algorithm can achieve fewer safety violations while showing better data efficiency compared with several baselines.
  arXiv  Detail & Related papers  (2023-02-07T16:53:33Z)
• Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study [7.8191100993403495]
  This paper defines a new set of network-level safety metrics for the overall safety assessment of traffic flow by processing imagery taken by roadside infrastructure sensors. An integrative analysis of the safety metrics and crash data reveals the insightful temporal and spatial correlation between the representative network-level safety metrics and the crash frequency.
  arXiv  Detail & Related papers  (2022-01-27T19:07:08Z)
• Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow [6.233422723925688]
  This paper develops a learning-based algorithm to deal with complex intersections with mixed traffic flows. We first consider different velocity models for green and red lights in the training process and use a finite state machine to handle different modes of light transformation. Then we design different types of distance constraints for vehicles, traffic lights, pedestrians, bicycles respectively and formulize the constrained optimal control problems.
  arXiv  Detail & Related papers  (2021-08-30T07:55:32Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Learning to be Safe: Deep RL with a Safety Critic [72.00568333130391]
  A natural first approach toward safe RL is to manually specify constraints on the policy's behavior. We propose to learn how to be safe in one set of tasks and environments, and then use that learned intuition to constrain future behaviors.
  arXiv  Detail & Related papers  (2020-10-27T20:53:20Z)

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.