Autonomous driving paper index
Deep CNN-Based Real-Time Traffic Light Detector for Self-Driving Vehicles
One-line summary
To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a lightweight and real-time traffic light detector for the autonomous vehicle platform.
Engineering notes
Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Due to the unavailability of Vehicle-to-Infrastructure (V2I) communication in current transportation systems, Traffic Light Detection (TLD) is still considered an important module in autonomous vehicles and Driver Assistance Systems (DAS). To overcome low flexibility and accuracy of vision-based heuristic algorithms and high power consumption of deep learning-based methods, we propose a lightweight and real-time traffic light detector for the autonomous vehicle platform. Our model consists of a heuristic candidate region selection module to identify all possible traffic lights, and a lightweight Convolution Neural Network (CNN) classifier to classify the results obtained. Offline simulations on the GPU server with the collected dataset and several public datasets show that our model achieves higher average accuracy and less time consumption. By integrating our detector module on NVidia Jetson TX1/TX2, we conduct on-road tests on two full-scale self-driving vehicle platforms (a car and a bus) in normal traffic conditions. Our model can achieve an average detection accuracy of 99.3 percent (mRttld) and 99.7 percent (Rttld) at 10Hz on TX1 and TX2, respectively. The on-road tests also show that our traffic light detection module can achieve <inline-formula><tex-math notation="LaTeX">$<\pm\; 1.5m$</tex-math><alternatives><mml:math><mml:mrow><mml:mo><</mml:mo><mml:mo>±</mml:mo><mml:mspace width="0.277778em"/><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mi>m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="guizani-ieq1-2892451.gif"/></alternatives></inline-formula> errors at stop lines when working with other self-driving modules.
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