Design and Implementation of Multi-Sensor Data Fusion Platform for Fuel Saving Lane-Changing Assistance

Abstract

The issues of intelligent vehicles are now widely discussed Increasing road traffic safety and at the same time reducing fuel consumption is one of the most challenging future tasks In order to improve road traffic safety the in-vehicle system (On-Board Unit OBU) must have the ability to perceive complex environment information and detect potential threats on the road For that reason we create a multi-sensor data fusion system to process environment information According to the paper in [20] with maximized traffic throughput the global fuel consumption will be improved in normal traffic conditions We develop a lane-changing mechanism that can make a lane-changing decision if there is a forward vehicle blocked the way we go forward and then we could not pass through the traffic light in front The purpose of lane-changing mechanism is to maximize traffic throughput for achieving global fuel saving by decreasing vehicles jammed on the road The data sources include IMU (Inertial Measurement Unit) ultrasound sensor lidar speed sensor camera RSU (Road Side Unit) OBD-II (On-Board Diagnostics) and GPS We use Kalman filter to stabilize significant parameters for adjusting safe distance while performing lane-change In addition to perceive surrounding of ego vehicle the traffic situation assessment of current road is also an important task So we develop a novel traffic situation assessment method based on Bayesian classification and a decision algorithm to decide whether the occasion is appropriate to change lane or not Finally we built the fusion system with user interface which can perform the recommendation of lane-change to the driver In the experiment results we analyzed and discussed about the performance of specific parameters estimation and traffic situation assessment Moreover the system’s response time (from the procedure of accessing raw data to the procedure of showing the lane-changing result) is approximately 0 3 second that measure up to our expectation

Date of Award	2016 Feb 18
Original language	English
Supervisor	Chung-Ping Young (Supervisor)