Abstract
The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning.
Highlights
The global status report on road safety 2018, launched by the WHO in December 2018, highlighted that the number of annual road traffic deaths had reached 1.35 million [1]
The straight driving experiments are conducted referring to JT/T883-2014, which describes the standard experiments for forward collision warning system (FCWS), published by the Ministry of Transport of the People’s Republic of China (MOT)
Since the collision warning system (CWS) is implemented based on the UWB/dead reckoning (DR) relative positioning/directing system, the positioning/directing accuracy can reflect the performance of the CWS
Summary
The global status report on road safety 2018, launched by the WHO in December 2018, highlighted that the number of annual road traffic deaths had reached 1.35 million [1]. The current V2V based CWSs implement relative positioning and communication separately using different technologies, e.g., predicting collision warning based on radars but communicating through WIFI, which may affect the real-time performance To address this issue, the UWB-based CCWS seamlessly combines CWS and V2V without delay. We define Zk as the observation vector, containing the relative position and orientation of vehicle 2 measured by the UWB system, four wheel-speeds measured by the DR system, and the observation noise Vk. the observation equation can be expressed as Equation (17). Racy of yaw rates and velocities estimated by UWB/DR to DR They are improved significantly as well, which contributes to the better prediction accuracy of TTC .
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