谷歌浏览器插件
订阅小程序
在清言上使用

A Spatial-State-Based Omni-Directional Collision Warning System for Intelligent Vehicles

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS(2024)

引用 0|浏览7
暂无评分
摘要
Collision warning systems (CWSs) have been recognized as effective tools in preventing vehicle collisions. Existing systems mainly provide safety warnings based on single-directional approaches, such as rear-end, lateral, and forward collision warnings. Such systems cannot provide omni-directorial enhancements on driver's perception. Meanwhile, due to the unclear and overlapped activation areas of above single-directional CWSs, multiple kinds of warnings may be triggered mistakenly for a collision. The multi-triggering may confuse drivers about the position of dangerous targets. To this end, this paper develops a spatial-state-based omni-directional collision warning system (S-OCWS), aiming to help drivers identify the specific danger by providing the unique warning. First, the operational domains of rear-end, lateral, and forward collisions are theoretically distinguished. This distinction is attained by a geometric approach with a rigorous mathematical derivation, based on the spatial states and the relative motion states of itself and the target vehicle in real time. Then, a theoretical omni-directional collision warning model is established using time-to-collision (TTC) to clarify activation conditions for different collision warnings. Finally, the effectiveness of the S-OCWS is validated in field tests. Results indicate that the S-OCWS can help drivers quickly and properly respond to the warnings without compromising their control over lateral offsets. In particular, the probability of drivers giving proper responses to FCW doubles when the S-OCWS is on, compared to when the system is off. In addition, the S-OCWS shortens the responses time of nonprofessional drivers, and therefore enhances their safety in driving.
更多
查看译文
关键词
Connected vehicles,omni-directional collision warning system,driving performance,field tests
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要