Abstract
Image recognition is susceptible to interference from the external environment. It is challenging to accurately and reliably recognize traffic lights in all-time and all-weather conditions. This article proposed an improved vision-based traffic lights recognition algorithm for autonomous driving, integrating deep learning and multi-sensor data fusion assist (MSDA). We introduce a method to obtain the best size of the region of interest (ROI) dynamically, including four aspects. First, based on multi-sensor data (RTK BDS/GPS, IMU, camera, and LiDAR) acquired in a normal environment, we generated a prior map that contained sufficient traffic lights information. And then, by analyzing the relationship between the error of the sensors and the optimal size of ROI, the adaptively dynamic adjustment (ADA) model was built. Furthermore, according to the multi-sensor data fusion positioning and ADA model, the optimal ROI can be obtained to predict the location of traffic lights. Finally, YOLOv4 is employed to extract and identify the image features. We evaluated our algorithm using a public data set and actual city road test at night. The experimental results demonstrate that the proposed algorithm has a relatively high accuracy rate in complex scenarios and can promote the engineering application of autonomous driving technology.
Highlights
In recent years, the rapid development of autonomous driving has attracted more and more attention worldwide
We proposed an improved traffic lights recognition algorithm based on the multi-sensor data fusion assist (MSDA)
(1) Based on the prior map and multi-sensor data, we propose a method of dynamically adjusting the size of region of interest (ROI) based on different positioning precision (RTK, degraded, outages), achieving the optimal auxiliary effect on the image recognition algorithm
Summary
The rapid development of autonomous driving has attracted more and more attention worldwide. The fusion of multiple technologies based on deep learning has become an important research field.[28] On one hand, combining traditional feature extraction and recognition methods with deep learning can make full use of their complementarity to improve system performance.[29] On the other hand, integrating real-time positioning and prior map data to obtain ROI and reduce environmental interference is helpful to improve the performance of the traffic lights recognition algorithm.[30]. The existing results mainly studied the image recognition of ordinary illumination and weather, which is difficult to meet the actual application requirements of autonomous driving To solve this question, we proposed an improved traffic lights recognition algorithm based on the multi-sensor data fusion assist (MSDA). The main innovations and achievements of this article were summarized, and further research work was discussed
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