Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Han, Yushan; Zhang, Hui; Li, Huifang; Jiang, Y.; Lang, Congyan; Li, Yidong

doi:10.48550/arxiv.2301.06262

Cited by 3 publications

(3 citation statements)

References 77 publications

(220 reference statements)

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“…Subject to sensor limitations, autonomous vehicles lack a global perception capability for monitoring holistic road conditions and accurately detecting surrounding objects, which bears great safety risks [1], [2]. Vehicle-to-everything (V2X) [3], [4] aims to build a communication system between vehicles and other devices in a complex traffic environment.…”

Section: Introductionmentioning

confidence: 99%

Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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In the study of network synchronization, an outstanding question of both theoretical and practical significance is how to allocate a given set of heterogenous oscillators on a complex network in order for improving the synchronization performance. Whereas methods have been proposed to address this question in literature, the methods are based on accurate models describing the system dynamics, which, however, are normally unavailable in realistic situations. Here we show that this question can be addressed by the model-free technique of feed-forward neural network (FNN) in machine learning. Specifically, we measure the synchronization performance of a number of allocation schemes and use the measured data to train a machine. It is found that the trained machine is able to not only infer the synchronization performance of any new allocation scheme, but also find from a huge amount of candidates the optimal allocation scheme for synchronization.

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Section: Introductionmentioning

confidence: 99%

Beijing Language and Culture University, China

Wang¹

2022

The Experience of Examining the PhD

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“…Cooperative Perception enables multiple agents to perceive surrounding environment collaboratively by sharing their observations and knowledge, which offers a great potential for improving individual's safety, resilience and adaptability [13,26,8,5,15]. Recent years have witnessed many related systems developed to support a broad range of real-world applications, e.g., vehicle-to-vehicle (V2V)communication-aided autonomous driving [37,38,33,2,3,5], multirobot warehouse automation system [17,41] and multiple unmanned aerial vehicles (UAVs) for search and rescue [29].…”

Section: Related Workmentioning

confidence: 99%

Cooperative control method of multi-missile formation under uncontrollable speed

Zhang

Lyu

et al. 2021

西北工业大学学报

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Under the missile speed is uncontrollable, a design method of multi-missile formation flight controller based on the sliding mode variable structure control theory and adaptive dynamic surface control theory is proposed. Firstly, according to the relative position of the leader and the follower in the inertial frame, the tracking error model for the relative position and the expected relative position between the leader and the follower is obtained, and the multi-missile formation control system in the inertial coordinate system is obtained. Secondly, in order to obtain the expression of the formation control system in the ballistic coordinate system, the acceleration of the missile in the ballistic coordinate system is converted to the inertial coordinate system. Combining with the tracking of the relative position and the desired relative position of the leader and the followers, we can obtain the simplified error model for the formation control system. Then the sliding mode variable structure control theory and the adaptive dynamic surface control theory are used to design the formation controllers for the leader and follower missiles respectively, and the stability of the present controller is analysed via the Lyapunov stability theory. Finally, the designed formation controllers are used for the leader and follower missiles to simulate the parameters. The results verify the feasibility and effectiveness of the present method.

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“…The recent development of deep learning brings significant breakthroughs in various perception tasks such as 3D object detection [22,35,42], object tracking [43,56], and semantic segmentation [47,57]. However, single-vehicle vision systems still suffer from many real-world challenges, such as occlusions and short-range perceiving capability [15,40,49], which * Corresponding Author, email address: x35xia@ucla.edu can cause catastrophic accidents. The shortcomings stem mainly from the limited field-of-view of the individual vehicle, leading to an incomplete understanding of the surrounding traffic.…”

Section: Introductionmentioning

confidence: 99%

V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception

Xu¹,

Xia²,

Li³

et al. 2023

Preprint

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Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception system has great potential to revolutionize the autonomous driving industry. However, the lack of a real-world dataset hinders the progress of this field. To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multimodal dataset for V2V perception. The data is collected by two vehicles equipped with multi-modal sensors driving together through diverse scenarios. Our V2V4Real dataset covers a driving area of 410 km, comprising 20K LiDAR frames, 40K RGB frames, 240K annotated 3D bounding boxes for 5 classes, and HDMaps that cover all the driving routes. V2V4Real introduces three perception tasks, including cooperative 3D object detection, cooperative 3D object tracking, and Sim2Real domain adaptation for cooperative perception. We provide comprehensive benchmarks of recent cooperative perception algorithms on three tasks. The V2V4Real dataset and codebase can be found at research.seas.ucla.edu/mobility-lab/v2v4real.

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Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges

Cited by 3 publications

References 77 publications

Beijing Language and Culture University, China

Beijing Language and Culture University, China

Cooperative control method of multi-missile formation under uncontrollable speed

V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception

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