车辆队列中使用车辆投影进行位置验证的可行性

Tasnuva Tithi, Chris Winstead, Ryan M. Gerdes
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引用次数: 3

摘要

这项工作调查了使用车辆投射的可见阴影来验证车辆在排中的位置声明的可行性。队列是一种引导一组车辆的方法,其中领头的车辆决定后面车辆的速度/速度。然后,追随者采用合作跟随策略来保持他们之间的期望分离。以这种方式,一组车辆作为一个单一的单位,这已被证明有许多安全和效率的好处。然而,现有的研究表明,队列中的破坏性成员能够导致队列中的其他成员增加其总能量消耗,甚至破坏队列的稳定,这可能导致灾难性事故。了解每辆车的位置和速度可以帮助阻止这种攻击,将破坏归因于造成破坏的特定车辆。实现这一目标的一种方法是假设领队车辆是可信的,并配备了摄像头,以便监视整个排并识别与报告位置/速度的偏差。由于排通常在直线上移动,领队可能无法直接看到排中的所有车辆。然而,在广泛的情况下,车辆的直接视图或车辆的阴影对领导者是可见的。在这项工作中,我们研究了通过单目摄像机观察到的阴影之间的差异距离是否可以揭示两辆车之间的距离随着时间的推移而变化,从而作为验证位置声明的机制。当无法直接看到车辆时,可以考虑使用阴影来检测车辆在各种天气和日光条件下的相对位置。我们的分析发现,阴影分析可以在连续图像中使用,以检测两辆车之间距离的实际变化,因为在非视觉场景中可见阴影。我们还提供了分析,以有效地使用该技术,因为太阳的位置在给定的地点全天变化。
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Viability of Using Shadows Cast by Vehicles for Position Verification in Vehicle Platooning
This work investigates the viability of using the visible shadows cast by vehicles to verify position claims made by vehicles in a platoon. Platooning is a method of guiding a group of vehicles whereby a lead vehicle determines the speed/velocity of the vehicles that follow. A cooperative following strategy is then employed by the followers to maintain a desired separation between themselves. In this way a group of vehicles acts as a single unit, which has been shown to have many safety and efficiency benefits. Existing work, however, demonstrates that a disruptive member of a platoon is capable of causing the rest of the platoon to increase its total energy expenditure, or even destabilizing the platoon, which could result in catastrophic accidents. Knowledge about the position and velocity of each vehicle can help deter such attacks by attributing the disruption to the specific vehicle causing it.One way to achieve this is to assume that the lead vehicle is trusted and equipped with a camera so as to watch over the platoon and identify deviations from reported positions/velocities. As platoons often move in a straight line, it might be impossible for the leader to obtain a direct view of all the vehicles in the platoon. Under a broad range of circumstances, however, a direct view of the vehicle or the shadows of the vehicles are visible to the leader. In this work we investigate whether the differential distance between shadows, as viewed through a monocular camera, can reveal that the distance between two vehicles has changed over time, and thus serve as a mechanism to verify positions claims. When a direct view of the vehicle is not achievable, the use of shadows to detect the relative positions of vehicles under a variety of weather and daylight conditions are considered. Our analysis finds that shadow analysis can be used in sequential images to detect practical changes in the distance between two vehicles for visible shadows in non-light-of-sightscenarios. We also present the analysis to efficiently use the technique as the position of the Sun changes through out the day for a given site location.
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