AICP: Augmented Informative Cooperative Perception

Zhou, Pengyuan; Kortoçi, Pranvera; Yau, Yui-Pan; Finley, Benjamin; Wang, Xiujun; Braud, Tristan; Lee, Lik-Hang; Tarkoma, Sasu; Kangasharju, Jussi; Hui, Pan

doi:10.1109/tits.2022.3155175

Cited by 20 publications

(7 citation statements)

References 61 publications

(53 reference statements)

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“…Such information could be distributed in a similar way to the cooperative perception mechanism, where each platoon member would also include in its message the information on the known other members’ parameters. Additionally, messages could be filtered before being relayed, e.g., based on their age or their relevance in the adaptation process, similarly to the approach proposed in [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications

Sroka

Ström

Svensson

et al. 2022

Sensors

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In this paper, we investigate the problem of reducing the use of radio resources for vehicle-to-vehicle communications in an autonomous platooning scenario. Achieving reliable communications, which is a key element allowing for the tight coordination of platoon vehicles’ motion, might be challenging in a case of heavy road traffic. Thus, in this paper, we propose to reduce the number of intra-platoon transmissions required to facilitate the safe autonomous control of vehicle mobility, by analyzing the impact of cars’ behaviors (in terms of acceleration changes) on the evolution of the inter-vehicle distance errors within the platoon. We derive formulas representing the relation between the platoon leader’s acceleration changes and the evolution of the distance error, velocity difference, and the accelerations for the first pair of vehicles. Furthermore, we propose a heuristic algorithm for selection of the intra-platoon messaging period for each platoon vehicle that minimizes the use of radio resources subject to the safety constraint, represented as the fraction of the total time when emergency braking is activated. The presented simulation results indicate that the proposed approach is capable of ensuring safe platoon operation and simultaneously providing a significant reduction in the use of resources, compared with conventional fixed-period transmission.

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Section: Discussionmentioning

confidence: 99%

Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications

Sroka

Ström

Svensson

et al. 2022

Sensors

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“…Meanwhile, Zhou et al suggested an enhanced cooperative information perception, in which an ultra-fast fltering system is proposed to optimize the received data. Tis means that, after fltering, only the information important for vehicle navigation is displayed, thereby eliminating visual noise and facilitating the understanding of the environment [48]. Additionally, an interesting approach was proposed by Cui et al where a tilt model is obtained based on intervehicle perception; in other words, frst, the information is encoded in a point-based representation to transmit compact messages between vehicles; secondly, the 3D LiDAR data from the diferent vehicles are aggregated and fnally analyzed [49].…”

Section: Cooperative Perceptionmentioning

confidence: 99%

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Palacios-Morocho,

Inca,

Monserrat

2024

Mobile Information Systems

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Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%.

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“…Specifically, ETSIs defines the message format and rules for CPMs generation [32,33]. In this way, CPMs allows CAVss to share surrounding information (e.g., objects detected) detected by sensors, such as, cameras, RADAR, LIDAR, and others [34,35]. A CPM contains data about the vehicle that generated the CPM, its onboard sensors (e.g., range and FoV), as well as data about the detected object (e.g., position, speed, distance, and size) [22,27].…”

Section: Cp Overviewmentioning

confidence: 99%

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

et al. 2023

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Due to poor local range of the perception and object recognition mechanisms used by autonomous vehicles, incorrect decisions can be made, which can jeopardize a fully autonomous operation. A connected and autonomous vehicle should be able to combine its local perception with the perceptions of other vehicles to improve its capability to detect and predict obstacles. Such a collective perception system aims to expand the field of view of autonomous vehicles, augmenting their decision-making process, and as a consequence, increasing driving safety. Regardless of the benefits of a collective perception system, autonomous vehicles must intelligently select which data should be shared with who and when in order to conserve network resources and maintain the overall perception accuracy and time usefulness. In this context, the operational impact and benefits of a redundancy reduction mechanism for collective perception among connected autonomous vehicles are analyzed in this article. Therefore, we propose a reliable redundancy mitigation mechanism for collective perception services to reduce the transmission of inefficient messages, which is called VILE. Knowledge, selection, and perception are the three phases of the cooperative perception process developed in VILE. The results have shown that VILE is able to reduce it the absolute number of redundant objects of 75% and generated packets by up to 55%. Finally, we discuss possible research challenges and trends.

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AICP: Augmented Informative Cooperative Perception

Cited by 20 publications

References 61 publications

Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications

Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles

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