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Forecasting approach in VANET based on vehicle kinematics for road safety

机译:基于车辆运动学的VANET道路安全预测方法

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This paper deals with the forecasting of collision events for road safety. Using significant parameters of each vehicle, such as position, speed and direction, it is possible to contribute to improving the road safety. We present a collaborative forecasting module in intersection scenario for collision avoidance. The proposed module is focused on the estimation of these parameters using a kinematic model of each vehicle to generate the trajectories estimation. The first simulation results show and assess that the vehicle trajectories estimated with the suggested kinematic modelling are realistic in all critical cases. The main goal of the suggested forecasting approach is to detect and avoid collision. On the basis of these trajectories estimation, the future occurrence of the collision event can be calculated, an alert must be generated and this will trigger the forecasting module in order to avoid collision. In addition, the second part of the simulation proves that the proposed forecast scenario is excellent for collision avoidance.
机译:本文涉及碰撞事件对道路安全的预测。使用每辆车的重要参数(例如位置,速度和方向),可以有助于改善道路安全性。为了避免碰撞,我们在交叉路口场景中提供了一个协作预测模块。所提出的模块集中在使用每辆车的运动学模型来生成轨迹估计的这些参数的估计上。最初的仿真结果表明并评估,在所有关键情况下,通过建议的运动学模型估算的车辆轨迹都是现实的。建议的预测方法的主要目标是检测并避免碰撞。根据这些轨迹估计,可以计算出碰撞事件的未来发生情况,必须生成警报,这将触发预测模块以避免碰撞。另外,仿真的第二部分证明了所提出的预测方案对于避免碰撞是极好的。

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