Investigation on Integration of Sensors and Vision-Based Vehicle Detection System for Autonomous Vehicle

At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy.

Download Full-text

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽

10.3390/s20216218 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6218

Author(s):

Rodrigo Carvalho Barbosa ◽

Muhammad Shoaib Ayub ◽

Renata Lopes Rosa ◽

Demóstenes Zegarra Rodríguez ◽

Lunchakorn Wuttisittikulkij

Keyword(s):

Deep Learning ◽

Traffic Control ◽

Detection System ◽

Lightweight Design ◽

Vehicle Detection ◽

Activation Function ◽

Urban Traffic ◽

Vehicle Classification ◽

Traffic Light ◽

Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

Download Full-text