Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles

Основные понятия

Developing an accuracy-aware cooperative sensing and computing scheme for connected autonomous vehicles.

Аннотация

The article proposes a scheme for accurate perception among connected autonomous vehicles (CAVs) by enabling fine-grained raw-level cooperative sensing and computing. It focuses on accuracy-aware data selection, joint subtask placement, and resource allocation to minimize resource costs while meeting delay and accuracy requirements. The proposed solution utilizes supervised learning models, optimization algorithms, and simulation results to demonstrate improved accuracy awareness and resource efficiency compared to benchmark solutions.

• Introduction to autonomous driving's foundation on CAVs' environment perception.
• Challenges of relying solely on onboard sensors for complete environment perception.
• Proposal of a cooperative sensing scheme leveraging V2X communication technologies.
• Trade-offs between resource efficiency and performance enhancement in cooperative sensing levels.
• Importance of scalable raw-level cooperative sensing for high perception performance.
• Development of an accuracy-aware cooperative sensing and computing scheme.
• Utilization of supervised learning models for object classification accuracy estimation.
• Formulation of an optimization problem for joint data selection, subtask placement, and resource allocation.
• Proposal of a genetic algorithm-based iterative solution for optimization problem resolution.

Статистика

Simulation results demonstrate the accuracy awareness achieved by the proposed scheme.

Цитаты

Ключевые выводы из

Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles

by Xuehan Ye,Ka... в arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16408.pdf

Дополнительные вопросы

How can the proposed accuracy-aware scheme impact the future development of autonomous driving systems

The proposed accuracy-aware scheme can have a significant impact on the future development of autonomous driving systems. By enabling cooperative sensing and computing among connected and autonomous vehicles (CAVs) and road-side infrastructure, the scheme enhances perception performance while optimizing resource efficiency. This can lead to improved object classification accuracy, reduced communication and computation costs, and enhanced overall system reliability. As autonomous driving heavily relies on accurate environment perception for safe navigation, the accuracy-aware scheme can contribute to advancing the capabilities of CAVs in complex real-world scenarios.

What potential challenges or limitations might arise from implementing this cooperative sensing approach

While the cooperative sensing approach offers several benefits, there are potential challenges and limitations that may arise during implementation. One challenge is ensuring seamless coordination among multiple CAVs and infrastructure components in dynamic environments with varying network conditions. Additionally, integrating learning-based accuracy estimation models into real-time decision-making processes may introduce complexity and computational overhead. Furthermore, maintaining data privacy and security while sharing sensitive information between vehicles could be a concern that needs to be addressed to prevent unauthorized access or malicious attacks.

How could advancements in vehicular edge computing technology further enhance the proposed solution

Advancements in vehicular edge computing technology can further enhance the proposed solution by providing additional computational resources closer to the point of data generation. By leveraging edge servers located near CAVs for processing intensive tasks such as object classification, latency can be minimized, leading to faster decision-making processes. Edge computing also enables distributed computing among CAVs and infrastructure nodes, allowing for parallelized processing of object classification subtasks at optimal locations based on network-wide resource availability. This decentralized approach not only improves system scalability but also reduces reliance on centralized servers for efficient data fusion and processing in cooperative sensing scenarios.