End-to-End Self-Driving Approach Independent of Irrelevant Roadside Objects With Auto-Encoder
On a highway, the frequency of occurrence of irrelevant features, such as trees, varies a lot in different scenes. A limitation of the deep conventional neural networks used in end-to-end self-driving systems is that if the incoming images contain too much information, it makes it difficult for the network to extract only the subset of features required for decision making. Consequently, while exi...
IEEE Transactions on Intelligent Transportation Systems
Feature extraction,Training,Task analysis,Roads,Decision making,Autonomous vehicles,Neural networks