Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems

• URL: http://arxiv.org/abs/2212.12819v1
• Date: Sat, 24 Dec 2022 22:03:08 GMT
• Title: Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems
• Authors: Rodolfo Valiente, Arash Raftari, Hossein Nourkhiz Mahjoub, Mahdi Razzaghpour, Syed K. Mahmud, Yaser P. Fallah
• Abstract summary: Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system. We propose a Context-Aware Target Classification (CA-TC) module and a hybrid learning-based predictive modeling technique for CVS systems.
• Score: 2.862606936691229
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

Related papers

• Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections [12.812518632907771]
  We present a novel framework that detects preemptively collisions at urban crossroads. We exploit the Multi-access Edge Computing platform of 5G networks.
  arXiv  Detail & Related papers  (2024-04-22T18:45:40Z)
• A V2X-based Privacy Preserving Federated Measuring and Learning System [0.0]
  We propose a federated measurement and learning system that provides real-time data to fellow vehicles over Vehicle-to-Vehicle (V2V) communication. We also operate a federated learning scheme over the Vehicle-to-Network (V2N) link to create a predictive model of the transportation network. Results indicate that the proposed FL scheme improves learning performance and prevents eavesdropping at the aggregator server side.
  arXiv  Detail & Related papers  (2024-01-24T23:11:11Z)
• 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)
• Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation [62.458968086881555]
  Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains. We propose a Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART) to address the challenge in CVDA.
  arXiv  Detail & Related papers  (2023-03-18T16:40:10Z)
• Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles [6.896682830421197]
  We design an integrated information sharing and safe multi-agent reinforcement learning framework for connected autonomous vehicles. We first use weight pruned convolutional neural networks (CNN) to process the raw image and point cloud LIDAR data locally at each autonomous vehicle. We then design a safe actor-critic algorithm that utilizes both a vehicle's local observation and the information received via V2V communication.
  arXiv  Detail & Related papers  (2023-02-08T20:31:41Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• 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)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• 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)
• 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.