Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion

• URL: http://arxiv.org/abs/2404.17697v1
• Date: Fri, 26 Apr 2024 20:54:44 GMT
• Title: Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion
• Authors: Thomas Billington, Ansh Gwash, Aadi Kothari, Lucas Izquierdo, Timothy Talty,
• Abstract summary: This paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles.

Related papers

• MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles [58.461077944514564]
  This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
  arXiv  Detail & Related papers  (2023-10-08T21:32:30Z)
• V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration [4.166623313248682]
  This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration. The proposed method aims to handle imperfect partial observations by fusing the onboard LiDAR sensor and V2X communication data.
  arXiv  Detail & Related papers  (2023-09-26T20:26:03Z)
• Experimental Assessment of a Forward-Collision Warning System Fusing Deep Learning and Decentralized Radio Sensing [0.0]
  This paper presents the idea of an automatic forward-collision warning system based on a decentralized radio sensing (RS) approach. In this framework, a vehicle in receiving mode employs a continuous waveform (CW) transmitted by a second vehicle as a probe signal to detect oncoming vehicles. Detection of oncoming vehicles is performed by a deep learning (DL) module that analyzes the features of the Doppler signature imprinted on the CW probe signal by a rapidly approaching vehicle.
  arXiv  Detail & Related papers  (2023-09-15T19:55:10Z)
• NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception [7.819255257787961]
  We introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures. To demonstrate the effectiveness of our solution, we design a new simulation framework to consider autonomous driving, sensor fusion and V2X communication in general.
  arXiv  Detail & Related papers  (2023-07-13T08:33:02Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• A Distributed Acoustic Sensor System for Intelligent Transportation using Deep Learning [2.1219631216034127]
  This work explores a novel data source based on optical fibre-based distributed acoustic sensors (DAS) for traffic analysis. We propose a deep learning technique to analyse DAS signals to address this challenge through continuous sensing and without exposing personal information. We achieve 92% vehicle classification accuracy and 92%-97% in occupancy detection based on DAS data collected under controlled conditions.
  arXiv  Detail & Related papers  (2022-09-13T13:23:30Z)
• 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)
• V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction [74.42961817119283]
  We use vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently aggregating the information received from multiple nearby vehicles, we can observe the same scene from different viewpoints.
  arXiv  Detail & Related papers  (2020-08-17T17:58:26Z)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)
• Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles [7.7003495898919265]
  We present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects. Our scheme mitigates the network load in vehicular communication networks and enhances the communication reliability.
  arXiv  Detail & Related papers  (2020-04-23T01:44:12Z)

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.