Prediction Based Decision Making for Autonomous Highway Driving

• URL: http://arxiv.org/abs/2209.02106v1
• Date: Mon, 5 Sep 2022 19:28:30 GMT
• Title: Prediction Based Decision Making for Autonomous Highway Driving
• Authors: Mustafa Yildirim, Sajjad Mozaffari, Luc McCutcheon, Mehrdad Dianati, Alireza Tamaddoni-Nezhad Saber Fallah
• Abstract summary: This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model. It considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving. The results show that the proposed PDRL model improves the decision-making performance compared to a Deep Reinforcement Learning (DRL) model by decreasing collision numbers.
• Score: 3.6818636539023175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 at any time to pass a slow vehicle or to help traffic flow. Anticipating the intention of surrounding vehicles, estimating their future states and integrating them into the decision-making process of an automated vehicle can enhance the reliability of autonomous driving in complex driving scenarios. This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving. The model is trained using real traffic data and tested in various traffic conditions through a simulation platform. The results show that the proposed PDRL model improves the decision-making performance compared to a Deep Reinforcement Learning (DRL) model by decreasing collision numbers, resulting in safer driving.

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)
• HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model [5.4854443795779355]
  This study presents a novel approach, HighwayLLM, which harnesses the reasoning capabilities of large language models (LLMs) to predict the future waypoints for ego-vehicle's navigation. Our approach also utilizes a pre-trained Reinforcement Learning (RL) model to serve as a high-level planner, making decisions on appropriate meta-level actions.
  arXiv  Detail & Related papers  (2024-05-22T11:32:37Z)
• 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)
• DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving [65.04871316921327]
  This paper introduces a new autonomous driving system that enhances the performance and reliability of autonomous driving system. DME-Driver utilizes a powerful vision language model as the decision-maker and a planning-oriented perception model as the control signal generator. By leveraging this dataset, our model achieves high-precision planning accuracy through a logical thinking process.
  arXiv  Detail & Related papers  (2024-01-08T03:06:02Z)
• DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning [7.2282857478457805]
  "DRNet" is a novel DRL-based framework that enables a DRL agent to learn to drive by executing reasonable lane changing on simulated highways. Our DRL agent has the ability to learn the desired task without causing collisions and outperforms DDQN and other baseline models.
  arXiv  Detail & Related papers  (2023-11-02T21:17:52Z)
• Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction [39.48631437946568]
  This paper focuses on the complex task of merging into moving traffic where uncertainty emanates from the behavior of other drivers. We frame the problem as a partially observable Markov decision process (POMDP) and solve it online with Monte Carlo tree search. The solution to the POMDP is a policy that performs high-level driving maneuvers, such as giving way to an approaching car, keeping a safe distance from the vehicle in front or merging into traffic.
  arXiv  Detail & Related papers  (2023-03-29T16:12:45Z)
• Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses [130.15554653948897]
  In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome. Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly. We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
  arXiv  Detail & Related papers  (2023-02-16T16:54:10Z)
• 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)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• 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)
• Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles [6.578495322360851]
  This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. Decision-making strategies related to similar tasks are transferable.
  arXiv  Detail & Related papers  (2020-09-07T17:34:01Z)

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.