Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication

• URL: http://arxiv.org/abs/2404.08013v1
• Date: Wed, 10 Apr 2024 15:37:15 GMT
• Title: Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication
• Authors: Ahmad Sarlak, Hazim Alzorgan, Sayed Pedram Haeri Boroujeni, Abolfazl Razi, Rahul Amin,
• Abstract summary: We propose a novel approach to realize an optimized Cooperative Perception (CP) under constrained communications. At the core of our approach is recruiting the best helper from the available list of front vehicles to augment the visual range. Our results demonstrate the efficacy of our two-step optimization process in improving the overall performance of cooperative perception.
• Score: 0.24466725954625887
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where their individual visual perception quality is compromised by adverse weather conditions (haze as foggy weather), low illumination, winding roads, and crowded traffic. To cover the limitations of former methods, in this paper, we propose a novel approach to realize an optimized CP under constrained communications. At the core of our approach is recruiting the best helper from the available list of front vehicles to augment the visual range and enhance the Object Detection (OD) accuracy of the ego vehicle. In this two-step process, we first select the helper vehicles that contribute the most to CP based on their visual range and lowest motion blur. Next, we implement a radio block optimization among the candidate vehicles to further improve communication efficiency. We specifically focus on pedestrian detection as an exemplary scenario. To validate our approach, we used the CARLA simulator to create a dataset of annotated videos for different driving scenarios where pedestrian detection is challenging for an AV with compromised vision. Our results demonstrate the efficacy of our two-step optimization process in improving the overall performance of cooperative perception in challenging scenarios, substantially improving driving safety under adverse conditions. Finally, we note that the networking assumptions are adopted from LTE Release 14 Mode 4 side-link communication, commonly used for Vehicle-to-Vehicle (V2V) communication. Nonetheless, our method is flexible and applicable to arbitrary V2V communications.

Related papers

• Direct-CP: Directed Collaborative Perception for Connected and Autonomous Vehicles via Proactive Attention [7.582576346284436]
  We propose Direct-CP, a proactive and direction-aware CP system aiming at improving CP in specific directions. Our key idea is to enable an ego vehicle to proactively signal its interested directions and readjust its attention to enhance local directional CP performance. Our approach achieves 19.8% higher local perception accuracy in interested directions and 2.5% higher overall perception accuracy than the state-of-the-art methods in collaborative 3D object detection tasks.
  arXiv  Detail & Related papers  (2024-09-13T13:53:52Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• 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)
• 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)
• 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)
• Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds [0.0]
  Cooperative perception allows a Connected Autonomous Vehicle to interact with the other CAVs in the vicinity. It can compensate for the limitations of the conventional vehicular perception such as blind spots, low resolution, and weather effects. We evaluate the performance of cooperative perception for both vehicle and pedestrian detection using the CODD dataset.
  arXiv  Detail & Related papers  (2022-07-30T01:53:05Z)
• 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)
• Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications [69.24726496448713]
  We propose an intelligent optimization framework based on the Markov Decision Process (MDP) to help the AV make optimal decisions. We then develop an effective learning algorithm leveraging recent advances of deep reinforcement learning techniques to find the optimal policy for the AV. We show that the proposed transferable deep reinforcement learning framework reduces the obstacle miss detection probability by the AV up to 67% compared to other conventional deep reinforcement learning approaches.
  arXiv  Detail & Related papers  (2021-05-28T08:45:37Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• 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)
• 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.