Design and Development of Vehicle Collision-Avoidance System Based on UWB Wireless Sensor Networks

Xu, Long; Feng, Jun; Lv, Xiao; Zhan, Yuan Sheng; Zhang, Da

doi:10.22323/1.299.0037

Cited by 1 publication

(1 citation statement)

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“…Sun et al [ 29 ] proposed a UWB/INS (Inertial Navigation System)-based automatic guided vehicle (AGV) collision avoidance system. Liu et al [ 30 ] designed a vehicle collision-avoidance system based on UWB wireless sensor networks. Marianna et al used UWB to obtain distance information and calculated the collision time to provide collision warnings for workers [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles

Wang

Chen

Jin

et al. 2021

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%