Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic Scenarios
Author:
Yang Fan1ORCID, Li Xueyuan1, Liu Qi1, Li Xiangyu1, Li Zirui1
Affiliation:
1. School of Mechanical Engineering, Beijing Institute of Technology, Zhongguancun South Street, Beijing 100081, China
Abstract
The decision-making algorithm serves as a fundamental component for advancing the level of autonomous driving. The end-to-end decision-making algorithm has a strong ability to process the original data, but it has grave uncertainty. However, other learning-based decision-making algorithms rely heavily on ideal state information and are entirely unsuitable for autonomous driving tasks in real-world scenarios with incomplete global information. Addressing this research gap, this paper proposes a stable hierarchical decision-making framework with images as the input. The first step of the framework is a model-based data encoder that converts the input image data into a fixed universal data format. Next is a state machine based on a time series Graph Convolutional Network (GCN), which is used to classify the current driving state. Finally, according to the state’s classification, the corresponding rule-based algorithm is selected for action generation. Through verification, the algorithm demonstrates the ability to perform autonomous driving tasks in different traffic scenarios without relying on global network information. Comparative experiments further confirm the effectiveness of the hierarchical framework, model-based image data encoder, and time series GCN.
Reference33 articles.
1. Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G.P., and Cho, W. (2022). A review on autonomous vehicles: Progress, methods and challenges. Electronics, 11. 2. Xiao, W., Mehdipour, N., Collin, A., Bin-Nun, A.Y., Frazzoli, E., Tebbens, R.D., and Belta, C. (2021, January 19–21). Rule-based optimal control for autonomous driving. Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems, Nashville, TN, USA. 3. Kim, J., Moon, S., Rohrbach, A., Darrell, T., and Canny, J. (2020, January 14–19). Advisable learning for self-driving vehicles by internalizing observation-to-action rules. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA. 4. Aksjonov, A., and Kyrki, V. (2021, January 19–22). Rule-based decision-making system for autonomous vehicles at intersections with mixed traffic environment. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA. 5. Decision-making method for vehicle longitudinal automatic driving based on reinforcement Q-learning;Gao;Int. J. Adv. Robot. Syst.,2019
|
|