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 11 publications

(3 citation statements)

References 22 publications

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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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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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“…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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“…Visual tracking technology has been widely used in intelligent video surveillance, unmanned driving, virtual reality, human-computer interaction and other civil and military fields. At present, with the support of hardware equipment, blockchain technology [2] and collaborative sensing technology [3], target tracking is truly developing towards intelligence and popularization. And because of that, the requirements for visual tracking technology are also getting higher and higher.…”

Section: Introductionmentioning

confidence: 99%

Anti-Similar Visual Target Tracking Algorithm Based on Filter Peak Guidance and Fusion Network

Wang

Yuan

et al. 2023

Electronics

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Visual tracking is a key research area in computer vision, as tracking technology is increasingly being applied in daily life, it has high-research significance. Visual tracking technology usually faces various challenging interference factors, among which, a similar background is one of the factors that has a greater impact on the tracking process. Kernelized Correlation Filter (KCF) tracking algorithm can track targets quickly by using circulant matrix, and has good tracking effect, so it is widely used in the tracking field. However, when the target is interfered by similar objects, the filter template in KCF cannot effectively distinguish between the target and the interfering object. This is because the filter only uses the texture gradient feature as the description object of the target, which will make the KCF algorithm extremely sensitive to the change of the target; therefore, the filter has difficultly making a judgment in the unstable scene, cannot accurately describe the target state, and finally leads to tracking failure. Therefore, this paper fuses Color Names (CN) on the basis of the original Histogram of Oriented Gradients (HOG) feature of KCF, which can obtain a more comprehensive feature representation, and realize the application of combined features to improve the anti-interference ability of KCF in complex scenes. In addition, this paper also uses the peak response of correlation filtering as the judgment condition to determine whether the current tracking result is stable. When the filter is in an unstable tracking state, the proposed algorithm will select the value with high confidence from its multiple responses as the candidate target of the Siamese network, and the deep learning network is used as the incremental learning method of the filter. The Channel Attention is introduced into the network layer, so that the network can adaptively reason and adjust the extracted universal features, and the enhanced feature information is used as the final discriminant basis. Finally, according to the response, the target with the smallest error compared with the target template is selected from multiple candidate targets as the final tracking result. The experimental results show that the average accuracy and average success rate of the proposed algorithm are significantly improved compared with the classical tracking algorithm, especially in dealing with similar target interference.

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Collaborative Perception for Autonomous Driving: Current Status and Future Trend

Cited by 11 publications

References 22 publications

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

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

Collaborative Perception—The Missing Piece in Realizing Fully Autonomous Driving

Anti-Similar Visual Target Tracking Algorithm Based on Filter Peak Guidance and Fusion Network

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