Apply Image Identification to Improve the Localization of the Self-Driving Vehicles
- Resource Type
- Conference
- Authors
- Liu, Shao-Hui; Huang, Shih-Yen
- Source
- 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2021 IEEE/ACIS 22nd International Conference on. :183-186 Nov, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Location awareness
Monte Carlo methods
Image recognition
Convolutional neural networks
Artificial intelligence
Software engineering
Deep learning
Visual Recognition
- Language
- ISSN
- 2693-8421
Location failure is dangerous for self-driving vehicles. Adaptive Monte Carlo Localization (AMCL)[1] provides wrong coordinates to the self-driving controller in some specific conditions. This paper proposed a scheme to solve this problem. This scheme provides a reference location to AMCL, which could exactly give coordinates to the self-driving controller. The experiment results showed that this reference location could improve the performance of AMCL to provide precise coordinates to the self-driving controller. In addition, to provide reference location to AMCL, this proposed scheme applied Convolutional Neural Network (CNN)[2] to identify the specific scenery front the vehicle. Accordingly, detect particular views will be another challenge for self-driving vehicles.