Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective

• URL: http://arxiv.org/abs/2403.15156v1
• Date: Fri, 22 Mar 2024 12:11:06 GMT
• Title: Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective
• Authors: Yukuan Jia, Jiawen Zhang, Shimeng Lu, Baokang Fan, Ruiqing Mao, Sheng Zhou, Zhisheng Niu,
• Abstract summary: Collaborative Perception can be applied to broaden the field of view of connected vehicles. We propose a BEV feature-based CP network architecture for infrastructure-assisted AVP systems.
• Score: 11.405406875019175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages. Although Collaborative Perception (CP) can be applied to broaden the field of view of connected vehicles, the limited bandwidth of vehicular communications restricts its application. In this work, we propose a BEV feature-based CP network architecture for infrastructure-assisted AVP systems. The model takes the roadside camera and LiDAR as optional inputs and adaptively fuses them with onboard sensors in a unified BEV representation. Autoencoder and downsampling are applied for channel-wise and spatial-wise dimension reduction, while sparsification and quantization further compress the feature map with little loss in data precision. Combining these techniques, the size of a BEV feature map is effectively compressed to fit in the feasible data rate of the NR-V2X network. With the synthetic AVP dataset, we observe that CP can effectively increase perception performance, especially for pedestrians. Moreover, the advantage of infrastructure-assisted CP is demonstrated in two typical safety-critical scenarios in the AVP setting, increasing the maximum safe cruising speed by up to 3m/s in both scenarios.

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)
• Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication [0.24466725954625887]
  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.
  arXiv  Detail & Related papers  (2024-04-10T15:37:15Z)
• Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes [70.08318779492944]
  We are the first to harness vanishing point (VP) priors for more effective segmentation. Our novel, efficient network for VSS, named VPSeg, incorporates two modules that utilize exactly this pair of static and dynamic VP priors.
  arXiv  Detail & Related papers  (2024-01-27T01:01:58Z)
• 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)
• 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)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Collaborative 3D Object Detection for Automatic Vehicle Systems via Learnable Communications [8.633120731620307]
  We propose a novel collaborative 3D object detection framework that consists of three components. Experiment results and bandwidth usage analysis demonstrate that our approach can save communication and computation costs.
  arXiv  Detail & Related papers  (2022-05-24T07:17:32Z)
• V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer [58.71845618090022]
  We build a holistic attention model, namely V2X-ViT, to fuse information across on-road agents. V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention. To validate our approach, we create a large-scale V2X perception dataset.
  arXiv  Detail & Related papers  (2022-03-20T20:18:25Z)

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.