Collaborative Perception for Autonomous Driving: Current Status and Future Trend

Ren, Shunli; Chen, Siheng; Zhang, Wenjun

doi:10.1007/978-981-19-3998-3_65

Cited by 18 publications

(5 citation statements)

References 22 publications

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“…Based on the stages at which data fusion occurs in the target detection model, fusion methods can be divided into early fusion, mid-level fusion, and late fusion [3]. Early fusion involves the fusion of initial data or preprocessed data.…”

Section: Cooperative Perceptionmentioning

confidence: 99%

Research on cooperative perception method based on heterogeneous graph attention network

Zhong,

Li,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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In this paper, we explore the use of cooperative perception to improve the perception performance of autonomous vehicles. By aggregating perception data from multiple nearby vehicles and roadside units, we can see through obstructions, detect objects at a distance, and improve detection accuracy. We propose a new cooperative perception framework, V2X-HAN. This framework mainly uses a heterogeneous graph attention network model, which can better capture the complex structure and rich information in the heterogeneous graph, achieve better feature fusion, and thus improve the accuracy of detection. We trained and validated the OPV2V and V2XSet datasets, and compared them with related models. Many experimental results show that V2X-HAN has achieved good detection results in cooperative perception.

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Section: Cooperative Perceptionmentioning

confidence: 99%

Research on cooperative perception method based on heterogeneous graph attention network

Zhong,

Li,

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…With the development of communication and smart city, the concept of Connected Vehicles (CVs), which connect with other vehicles, pedestrians, and infrastructures, are often realized with Autonomous Vehicles (AVs) simultaneously [2]. CVs provide cooperative perception that is necessary to the fully AVs which do not rely on human supervision [17]. The cooperative perception may also benefit the defense of LiDAR attack by providing redundant information to neighboring vehicles.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks

Zhang¹,

Li²,

Cheng³

et al. 2023

Proceedings Inaugural International Symposium on Vehicle Security &Amp; Privacy

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Autonomous vehicles rely on LiDAR sensors to detect obstacles such as pedestrians, other vehicles, and fixed infrastructures. LiDAR spoofing attacks have been demonstrated that either create erroneous obstacles or prevent detection of real obstacles, resulting in unsafe driving behaviors. In this paper, we propose an approach to detect and mitigate LiDAR spoofing attacks by leveraging LiDAR scan data from other neighboring vehicles. This approach exploits the fact that spoofing attacks can typically only be mounted on one vehicle at a time, and introduce additional points into the victim's scan that can be readily detected by comparison from other, non-modified scans. We develop a Fault Detection, Identification, and Isolation procedure that identifies non-existing obstacle, physical removal, and adversarial object attacks, while also estimating the actual locations of obstacles. We propose a control algorithm that guarantees that these estimated object locations are avoided. We validate our framework using a CARLA simulation study, in which we verify that our FDII algorithm correctly detects each attack pattern.

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“…The position information serves to align the transformation and the collaboration module performs specific fusion operations. Based on the message delivery and the stage of collaboration, the CP approach is broadly classified into three types [ 51 ]. In what follows next, we briefly discuss these CP message-sharing models.…”

Section: Collaborative Perception Sharingmentioning

confidence: 99%

Collaborative Perception—The Missing Piece in Realizing Fully Autonomous Driving

Malik,

Khan,

Khan

et al. 2023

Sensors

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Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems’ constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation. CP leverages vehicle-to-vehicle and vehicle-to-infrastructure communication to enable vehicles and infrastructure to combine and share information to comprehend the surrounding environment beyond the line of sight and field of view to enhance perception accuracy, lower latency, and remove perception blind spots. In this article, we highlight the need for an evolved version of the collaborative perception that should address the challenges hindering the realization of level 5 AD use cases by comprehensively studying the transition from classical perception to collaborative perception. In particular, we discuss and review perception creation at two different levels: vehicle and infrastructure. Furthermore, we also study the communication technologies and three different collaborative perception message-sharing models, their comparison analyzing the trade-off between the accuracy of the transmitted data and the communication bandwidth used for data transmission, and the challenges therein. Finally, we discuss a range of crucial challenges and future directions of collaborative perception that need to be addressed before a higher level of autonomy hits the roads.

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Collaborative Perception for Autonomous Driving: Current Status and Future Trend

Cited by 18 publications

References 22 publications

Research on cooperative perception method based on heterogeneous graph attention network

Research on cooperative perception method based on heterogeneous graph attention network

Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks

Collaborative Perception—The Missing Piece in Realizing Fully Autonomous Driving

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