Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation

• URL: http://arxiv.org/abs/2307.02663v1
• Date: Wed, 5 Jul 2023 21:38:36 GMT
• Title: Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation
• Authors: Tengchan Zeng, Aidin Ferdowsi, Omid Semiari, Walid Saad, Choong Seon Hong
• Abstract summary: 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.
• Score: 78.60496411542549
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to autonomously navigate to target destinations. To this end, each CAV's navigation controller must leverage the information collected by sensors and wireless systems for decision-making on longitudinal and lateral movements. However, enabling autonomous navigation for CAVs requires a convergent integration of communication, control, and learning systems. The goal of this article is to explicitly expose the challenges related to this convergence and propose solutions to address them in two major use cases: Uncoordinated and coordinated CAVs. In particular, challenges related to the navigation of uncoordinated CAVs include stable path tracking, robust control against cyber-physical attacks, and adaptive navigation controller design. Meanwhile, when multiple CAVs coordinate their movements during navigation, fundamental problems such as stable formation, fast collaborative learning, and distributed intrusion detection are analyzed. For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation. Preliminary simulation results are provided to show the merits of proposed solutions.

Related papers

• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots [50.02055068660255]
  Navigating urban environments poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain.
  arXiv  Detail & Related papers  (2024-05-03T00:29:20Z)
• Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review [0.0]
  Unmanned Aerial Vehicle (UAV) is used in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously - without human interaction, perform specific tasks and avoid obstacles. This study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems.
  arXiv  Detail & Related papers  (2022-08-25T20:04:11Z)
• Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies [14.518558523319518]
  Vehicle-to-Infrastructure (V2I) communication is becoming critical for the enhanced reliability of autonomous vehicles (AVs) It is critical to simultaneously optimize the AVs' network selection and driving policies in order to minimize road collisions. We develop a reinforcement learning framework to characterize efficient network selection and autonomous driving policies.
  arXiv  Detail & Related papers  (2022-08-03T04:33:02Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• 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)
• Deep Learning Based Intelligent Inter-Vehicle Distance Control for 6G Enabled Cooperative Autonomous Driving [7.599093591763697]
  Connected autonomous driving (CAV) is a critical vertical envisioned for 6G, holding great potentials of improving road safety, road and energy efficiency. New channel access algorithms and intelligent control schemes for connected vehicles are needed for 6G supported CAV. A deep learning neural network is developed and trained for fast computation of the delay bounds in real time operations.
  arXiv  Detail & Related papers  (2020-12-26T21:38:16Z)
• 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)
• Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion [2.0010674945048468]
  We present an RL-based multi-agent CAV control model to operate in mixed traffic. The results suggest that even at CAV percent share of corridor traffic as low as 10%, CAVs can significantly mitigate bottlenecks in highway traffic.
  arXiv  Detail & Related papers  (2020-10-12T03:52:10Z)
• A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles [21.132777568170702]
  We design an information-sharing-based reinforcement learning framework for connected autonomous vehicles. We show that our approach can improve the CAV system's efficiency in terms of average velocity and comfort. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.
  arXiv  Detail & Related papers  (2020-03-09T19:15:30Z)

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.