Name
Abstract | ||
|---|---|---|
The field of autonomous driving has seen increasing proposed use of machine learning methodologies. However, there are still challenges in applying such methods since autonomous driving involves complex and dynamic interactions with the environment. Supervised learning algorithms such as imitation learning can work in environments represented in the training data set, however, it is impractical or... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IV48863.2021.9575379 | 2021 IEEE Intelligent Vehicles Symposium (IV) |
Keywords | DocType | ISSN |
Training,Adaptation models,Simulation,Supervised learning,Training data,Reinforcement learning,Benchmark testing | Conference | 1931-0587 |
ISBN | Citations | PageRank |
978-1-7281-5394-0 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Ye | 1 | 1 | 0.36 |
| Pin Wang | 2 | 2 | 0.70 |
| Ching-Yao Chan | 3 | 79 | 23.48 |
| Jiucai Zhang | 4 | 1 | 1.03 |