COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles

Cui, Jiaxun; Qiu, Hang; Chen, Dian; Stone, Peter; Zhu, Yuke

doi:10.48550/arxiv.2205.02222

Cited by 1 publication

(1 citation statement)

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“…V2X Perception: V2X perception investigates how to leverage the visual information from nearby AVs and intelligent infrastructure to enhance the perception capability. Based on the collaboration strategies, there are three major classes: early [21], late [22], [23], [24], and intermediate fusion [5], [7], [10], [4], [25], [26], [27]. The early fusion method delivers the raw point clouds across agents, and each agent will feed the aggregated point clouds to the network for 3D detection.…”

Section: Related Workmentioning

confidence: 99%

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Xiang¹,

Xu²,

Xia³

et al. 2022

Preprint

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Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With the rapid growth of autonomous vehicles and intelligent infrastructure, the V2X perception systems will soon be deployed at scale, which raises a safety-critical question: how can we evaluate and improve its performance under challenging traffic scenarios before the real-world deployment? Collecting diverse large-scale real-world test scenes seems to be the most straightforward solution, but it is expensive and timeconsuming, and the collections can only cover limited scenarios. To this end, we propose the first open adversarial scene generator V2XP-ASG that can produce realistic, challenging scenes for modern LiDAR-based multi-agent perception system. V2XP-ASG learns to construct an adversarial collaboration graph and simultaneously perturb multiple agents' poses in an adversarial and plausible manner. The experiments demonstrate that V2XP-ASG can effectively identify challenging scenes for a large range of V2X perception systems. Meanwhile, by training on the limited number of generated challenging scenes, the accuracy of V2X perception systems can be further improved by 12.3% on challenging and 4% on normal scenes.

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