Advanced Road Safety: Collective Perception for Probability of Collision Estimation of Connected Vehicles

IF 2.6 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sabrine Belmekki, Dominique Gruyer
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Abstract

In the dynamic landscape of vehicular communication systems, connected vehicles (CVs) present unprecedented capabilities in perception, cooperation, and, notably, probability of collision management. This paper’s main concern is the collision probability of collision estimation. Achieving effective collision estimation heavily relies on the sensor perception of obstacles and a critical collision probability prediction system. This paper is dedicated to refining the estimation of collision probability through the intentional integration of CV communications, with a specific focus on the collective perception of connected vehicles. The primary objective is to enhance the understanding of the potential probability of collisions in the surrounding environment by harnessing the collective insights gathered through inter-vehicular communication and collaboration. This improvement enables a superior anticipation capacity for both the driving system and the human driver, thereby enhancing road safety. Furthermore, the incorporation of extended perception strategies holds the potential for more accurate collision probability estimation, providing the driving system or human driver with increased time to react and make informed decisions, further fortifying road safety measures. The results underscore a significant enhancement in collision probability awareness, as connected vehicles collectively contribute to a more comprehensive collision probability landscape. Consequently, this heightened collective collision probability perception improves the anticipation capacity of both the driving system and the human driver, contributing to an elevated level of road safety. For future work, the exploration of our extended perception techniques to achieve real-time probability of collision estimation is proposed. Such endeavors aim to drive the development of robust and anticipatory autonomous driving systems that truly harness the benefits of connected vehicle technologies.
先进的道路安全:联网车辆碰撞概率估算的集体感知
在车辆通信系统的动态环境中,互联车辆(CV)在感知、合作,尤其是碰撞概率管理方面展现出前所未有的能力。本文主要关注碰撞估计的碰撞概率。实现有效的碰撞估计在很大程度上依赖于传感器对障碍物的感知和关键的碰撞概率预测系统。本文致力于通过有意整合 CV 通信来完善碰撞概率的估计,并特别关注互联车辆的集体感知。其主要目的是利用通过车辆间通信和协作收集的集体感知,加强对周围环境中潜在碰撞概率的了解。通过这种改进,驾驶系统和人类驾驶员都能提高预测能力,从而加强道路安全。此外,扩展感知策略的采用有可能实现更准确的碰撞概率估计,为驾驶系统或人类驾驶员提供更多时间做出反应和明智决策,进一步强化道路安全措施。研究结果表明,由于互联车辆共同促成了更全面的碰撞概率图谱,因此碰撞概率感知能力显著增强。因此,这种增强的集体碰撞概率感知提高了驾驶系统和人类驾驶员的预测能力,有助于提升道路安全水平。在未来的工作中,我们建议探索我们的扩展感知技术,以实现实时碰撞概率估计。这些努力旨在推动稳健且具有预见性的自动驾驶系统的发展,从而真正利用互联汽车技术的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers
Computers COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.40
自引率
3.60%
发文量
153
审稿时长
11 weeks
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