Efficient and Unbiased Safety Test for Autonomous Driving Systems

Jiang, Zhengmin; Pan, Wenbo; Liu, Jia; Dang, Shaobo; Yang, Zhiheng; Li, Huiyun; Pan, Yi

doi:10.1109/tiv.2022.3213310

Cited by 6 publications

(1 citation statement)

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“…Autonomous vehicles (AVs) hold great promise to make the world a safer place by reducing accident rates [1]. Among the various enabling functionalities in AVs, accurate representation and mapping of the driving environment is the first and foremost task, offering pertinent information to facilitate informed decision-making in complex environments [2].…”

Section: Introductionmentioning

confidence: 99%

CooPercept: Cooperative Perception for 3D Object Detection of Autonomous Vehicles

Zhang,

Chen,

Qin

et al. 2024

Drones

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Autonomous vehicles rely extensively on onboard sensors to perceive their surrounding environments for motion planning and vehicle control. Despite recent advancements, prevalent perception algorithms typically utilize data acquired from the single host vehicle, which can lead to challenges such as sensor data sparsity, field-of-view limitations, and occlusion. To address these issues and enhance the perception capabilities of autonomous driving systems, we explore the concept of multi-vehicle multimedia cooperative perception by investigating the fusion of LiDAR point clouds and camera images from multiple interconnected vehicles with different positions and viewing angles. Specifically, we introduce a semantic point cloud feature-level cooperative perception framework, termed CooPercept, designed to mitigate computing complexity and reduce turnaround time. This is crucial, as the volume of raw sensor data traffic generally far exceeds the bandwidth of existing vehicular networks. Our approach is validated through experiments conducted on synthetic datasets from KITTI and OPV2V. The results demonstrate that our proposed CooPercept model surpasses comparable perception models, achieving enhanced detection accuracy and greater detection robustness.

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Section: Introductionmentioning

confidence: 99%