Hybrid AI boosts autonomous vehicle safety by detecting traffic signs faster
Researchers combined camera images with LiDAR point cloud data to identify traffic signs with 10% higher accuracy than previous methods, reducing missed detections that could compromise self-driving vehicle safety. The breakthrough matters for automakers and autonomous vehicle operators racing to deploy systems that reliably navigate real-world road conditions.
Originaltitel: Extracting Traffic Signage by Combining Point Clouds and Images
<p>Recognizing traffic signs is key to achieving safe automatic driving. With the decreasing cost of LiDAR, the accurate extraction of traffic signs using point cloud data has received wide attention. In this study, we propose combining point cloud and image traffic sign extraction: firstly, we use the improved YoloV3 model to detect traffic signs in panoramic images. The specific improvement is that the convolution block attention module is added to the algorithm framework, the traditional K-means clustering algorithm is improved, and Focal Loss is introduced as the loss function. It shows higher accuracy on the TT100K dataset, with a 1.4% improvement in accuracy compared to the previous YoloV3. Then, the point cloud of the area where the traffic sign is located is extracted by combining the image detection results. On this basis, the outline of the traffic sign is accurately extracted using the reflection intensity, spatial geometry and other information. Compared with the traditional method, the proposed method can effectively reduce the missed detection rate, narrow the range of point cloud, and improve the detection accuracy by 10.2%.</p>