Abstract
Recent emerging automotive sensors and innovative technologies in Advanced Driver Assistance Systems (ADAS) increase the safety of driving a vehicle on the road. ADAS enhance road safety by providing early warning signals for drivers and controlling a vehicle accordingly to mitigate a collision. A Rear Cross Traffic (RCT) detection system is an important application of ADAS. Rear-end crashes are a frequently occurring type of collision, and approximately 29.7% of all crashes are rear-ended collisions. The RCT detection system detects obstacles at the rear while the car is backing up. In this paper, a robust sensor fused RCT detection system is proposed. By combining the information from two radars and a wide-angle camera, the locations of the target objects are identified using the proposed sensor fused algorithm. Then, the transferred Convolution Neural Network (CNN) model is used to classify the object type. The experiments show that the proposed sensor fused RCT detection system reduced the processing time 15.34 times faster than the camera-only system. The proposed system has achieved 96.42% accuracy. The experimental results demonstrate that the proposed sensor fused system has robust object detection accuracy and fast processing time, which is vital for deploying the ADAS system.
Highlights
Most traffic accidents occurred due to human error
This paper proposes a robust and cost-effective Rear-Cross Traffic (RCT) detection system by fusing information from the rearview camera and short-range radars
This paper proposes a robust sensor fused RCT detection system by combining the information from two radars and a wide-angle camera sensor
Summary
Most traffic accidents occurred due to human error. Rear-end crashes are a frequently occurring type of collision, and approximately 29.7% of all crashes are rear-ended collisions [1]. By fusing the information from camera and radar sensors, the RCT detection system’s detection range is expanded, and the system accuracy can be improved. Many vehicles are equipped with a rearview camera, and it has become a trend to mount short-range radars on the rear bumper for object detection in blind spots. This paper proposes a robust and cost-effective RCT detection system by fusing information from the rearview camera and short-range radars. The proposed system combines signals from two radars mounted on the left and right sides on the rear bumper These combined radar signals are fused with the information from the camera sensor to detect the rear-end obstacles.
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