A new multisensor network for collision avoidance and jackknife prevention of articulated vehicles using Lebesgue sampling

Sensor networks are increasingly used in advanced vehicle and transportation applications. It is desirable in many systems to minimize sensor power requirements. Motivated by the flexibility available with battery powered wireless sensors, this paper presents a new sensor network communication method that significantly reduces the occurrence of message transmissions. This is achieved by applying Lebesgue sampling theory to detect transitions in signal levels between quantized states. Sensor outputs are broadcasted only when significant information has been acquired as a state transition. Consequently, power consumed throughout the sensor network is minimized. Results for a large truck vehicle stability enhancement system are given. The feedback control system is tested in a laboratory system with an SAE-J1939 compliant commercial vehicle network, with a virtual truck implementation for evaluating the effectiveness of the sensor network technique.