Vignarca Daniele, Arrigoni Stefano, Sabbioni Edoardo
Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy.
Sensors (Basel). 2023 Aug 3;23(15):6888. doi: 10.3390/s23156888.
The recent advancements in Intelligent Transportation Systems (ITS) have revealed significant potential for enhancing traffic management through Advanced Driver Assist Systems (ADASs), with benefits for both safety and environment. This research paper proposes a vehicle localization technique based on Kalman filtering, as accurate positioning of the ego-vehicle is essential for the proper functioning of the Traffic Light Advisor (TLA) system. The aim of the TLA is to calculate the most suitable speed to safely reach and pass the first traffic light in front of the vehicle and subsequently keep that velocity constant to overcome the following traffic light, thus allowing safer and more efficient driving practices, thereby reducing safety risks, and minimizing energy consumption. To overcome Global Positioning Systems (GPS) limitations encountered in urban scenarios, a multi-rate sensor fusion approach based on the Kalman filter with map matching and a simple kinematic one-dimensional model is proposed. The experimental results demonstrate an estimation error below 0.5 m on urban roads with GPS signal loss areas, making it suitable for TLA application. The experimental validation of the Traffic Light Advisor system confirmed the expected benefits with a 40% decrease in energy consumption compared to unassisted driving.
智能交通系统（ITS）的最新进展显示出通过高级驾驶员辅助系统（ADAS）增强交通管理的巨大潜力，这对安全和环境都有益处。本研究论文提出了一种基于卡尔曼滤波的车辆定位技术，因为自车的精确定位对于交通信号灯辅助系统（TLA）的正常运行至关重要。TLA的目标是计算出最适合的速度，以便安全到达并通过车辆前方的第一个交通信号灯，随后保持该速度恒定以通过后续的交通信号灯，从而实现更安全、更高效的驾驶方式，进而降低安全风险并将能耗降至最低。为了克服在城市场景中遇到的全球定位系统（GPS）限制，提出了一种基于卡尔曼滤波、地图匹配和简单运动学一维模型的多速率传感器融合方法。实验结果表明，在存在GPS信号丢失区域的城市道路上，估计误差低于0.5米，这使其适用于TLA应用。交通信号灯辅助系统的实验验证证实了预期的益处，与无辅助驾驶相比，能耗降低了40%。
