The Components of Cooperative Perception - a Proposal for Future Works

Pilz, Christoph; Ulbel, Andrea; Steinbauer-Wagner, Gerald

doi:10.1109/itsc48978.2021.9564989

Cited by 10 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These standards focus on the information that can be exchanged to provide a safer and more efficient use of the transportation network, such as road layout, safety alerts, signal phase and timing, and data on other road users and vehicles. Evidently, the communication component of V2X requires in-depth testing, analysis [5], and simulation [6], as bandwidth and latency can play a major role in the overall quality of the system [7]. In this paper, we focus on the task of V2X-enabled CP, i.e., multiple perception-capable units such as On-board Perception (OBP) and Roadside Perception Units (RSPU) augment their perception via V2X communication [8].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Piazzoni

Cherian

Vijay

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

In this paper, we introduce the notion of Cooperative Perception Error Models (coPEMs) towards achieving an effective and efficient integration of V2X solutions within a virtual test environment. We focus our analysis on the occlusion problem in the (onboard) perception of Autonomous Vehicles (AV), which can manifest as misdetection errors on the occluded objects. Cooperative perception (CP) solutions based on Vehicle-to-Everything (V2X) communications aim to avoid such issues by cooperatively leveraging additional points of view for the world around the AV. This approach usually requires many sensors, mainly cameras and LiDARs, to be deployed simultaneously in the environment either as part of the road infrastructure or on other traffic vehicles. However, implementing a large number of sensor models in a virtual simulation pipeline is often prohibitively computationally expensive. Therefore, in this paper, we rely on extending Perception Error Models (PEMs) to efficiently implement such cooperative perception solutions along with the errors and uncertainties associated with them. We demonstrate the approach by comparing the safety achievable by an AV challenged with a traffic scenario where occlusion is the primary cause of a potential collision.

show abstract

Section: Related Workmentioning

confidence: 99%

“…As previously mentioned, latency can be a significant bottleneck in V2X solutions [7]. Although it is not an issue in a virtual environment, latency can be specifically modeled and introduced to improve realism in virtual V2X testing.…”

Section: V2x Latencymentioning

confidence: 99%

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Piazzoni

Cherian

Vijay

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…By transmitting more CPMs than CAMs, CAVs can perceive more surrounding objects because they include information on multiple perceived objects by in-vehicle sensors into CPMs. However, the information accuracy of in-vehicle sensors is influenced by several factors such as brightness and weather conditions [2], [3], [4]. In contrast, by transmitting more CAMs than CPMs, CAVs can make the information in CPMs more accurate since the CAVs supply their own driving conditions to the CAMs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Message Prioritization for Vehicular Cooperative Perception at Target Intervals

Kunibe

Yamazaki

Murakawa

et al. 2023

Journal of Information Processing

View full text Add to dashboard Cite

In this paper, we propose adaptive message prioritization for vehicular cooperative perception at target intervals. The purpose of this paper is to ensure that Connected and Autonomous Vehicles (CAVs) can perceive the surrounding traffic environment by exchanging wireless messages such as Cooperative Awareness Messages (CAMs) and Cooperative Perception Messages (CPMs). Especially, under challenging conditions such as the wireless congestion control or the mixed-traffic of CAVs and human-driven vehicles, the number of perceptible objects by these messages decreases due to the message transmission rate control under the congestion control or the less message transmission under the low CAV penetration rate. To perceive the surrounding objects under the challenging conditions, the proposed method assigns high transmission priority to CPMs that include the information of multiple objects perceived by in-vehicle sensors. Specifically, the method controls the transmission frequency of CAMs and CPMs by assigning transmission priority to them such that the maximum perception interval approaches the target interval. Simulation result shows that 75% CAVs perceive surrounding vehicles within the target interval even under both the wireless congestion control and 20% CAV penetration rate.

show abstract

“…In order to increase their safety, vehicles are increasingly reliant on advanced technologies such as processing, sensors, and communication [Damaj et al 2021]. CAVs are designed to enhance driving safety, traffic efficiency, and the utilization of public resources such as roads, highways, and public transportation, as well as CAVs become a interest topic for both academia and industry [Pilz et al 2021]. CAVs also issue warnings or take action during risky events or maneuvers, allowing drivers to be relieved of the stressful task of driving and providing them with physical and mental relaxation [Lobato et al 2023].…”

Section: Introductionmentioning

confidence: 99%

Entropy-based Client Selection Mechanism for Vehicular Federated Environments

Sousa¹,

Lobato

Rosário³

et al. 2023

Anais Do XXII Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance 2023)

View full text Add to dashboard Cite

Autonomous driving requires machine learning models to be trained at the edge for improved efficiency and reduced communication latency. Federated learning (FL) allows knowledge sharing among all devices, but Not Independent and Identically Distributed (non-IID) scenarios with biased device data distributions can lead to statistical heterogeneity and lower classification accuracy. This paper proposes an entropy-based client selection approach for vehicular federated learning environments that aims to address the challenges posed by non-IID data in vehicular networks. The proposed method is compared to a random selection mechanism in both IID and non-IID scenarios, as well as in a scenario with random client drops. The results show that the entropy-based selection method outperforms the random selection method in all compared metrics, particularly in non-IID scenarios.

show abstract

The Components of Cooperative Perception - a Proposal for Future Works

Cited by 10 publications

References 47 publications

CoPEM: Cooperative Perception Error Models for Autonomous Driving

CoPEM: Cooperative Perception Error Models for Autonomous Driving

Adaptive Message Prioritization for Vehicular Cooperative Perception at Target Intervals

Entropy-based Client Selection Mechanism for Vehicular Federated Environments

Contact Info

Product

Resources

About