Recently, semantic segmentation technology has developed rapidly and is gradually applied in the field of autonomous driving. In this paper, DeepLabV3+ semantic segmentation is applied to the driverless system to realize the classification of road scene images at pixel level accuracy, so as to improve the accuracy of collected road information. Then, an automatic control strategy based on the Ackerman unmanned vehicle model with cascade PID front wheel steering control and rear wheel differential control is established to ensure that the vehicle can make the correct decision after receiving the traffic information. The cooperation of the above two technologies jointly realizes the automatic driving safely.
Unmanned Driving System Based on DeepLabV3+ Semantic Segmentation
2021-05-28
852435 byte
Conference paper
Electronic Resource
English
Lightning Arrester Target Segmentation Algorithm Based on Improved DeepLabv3+ and GrabCut
British Library Conference Proceedings | 2022
|Semantic Image Segmentation on Snow Driving Scenarios
British Library Conference Proceedings | 2020
|Obstacle detection based on semantic segmentation for unmanned vehicles in traffic scenes
British Library Conference Proceedings | 2022
|