With the booming development of autonomous vehicles (AV) in recent years, a vehicle needs to have the ability to detect changes in the environment in real-time. If the vehicle can be decelerated in advance according to the traffic signs, it can effectively reduce fuel consumption and improve overall comfort. This paper uses the Kaggle data set for training based on marking the common traffic signs in foreign countries, adds the local data set in Taiwan to the testing data set, and uses the You Only Look Once v4 (YOLOv4) neural network to detect the traffic signs in real time. The experimental results show that YOLOv4 still has a good generalization ability in the case of slight differences in different national sign types, and the mean Average Precision (mAP) can reach more than 87.6%.

This research focuses on integrating low-cost 2D-Lidar into a multi-split laser vaporization system that can incinerate wastes and general objects to improve the problem of manual alignment of the laser vaporization machine. In the experiment, we proposed a custom angle range and a distance threshold screening method for 2D-Lidar to distinguish whether there is an object in the scanning area and object locating. Through the law of cosine, the 2D-Lidar scanning angle is evenly distributed to the multi-split laser vaporization system. The object scanning method uses the Modbus TCP communication mode to enable the 2D-Lidar on the PC side to handshaking with the PLC on the central control side of the system. Scanning and size calculation of the object can obtain the position point of the object and hence improving the efficiency of the multi-split laser vaporization system. In addition, 2D lidar is readily available and inexpensive, in which making it more cost-effective than 3D-Lidar.