End-to-end driving simulation via angle branched network

Understand

Imitation learning for end-to-end autonomous driving has drawn attention from academic communities.

• Current methods either only use images as the input which is ambiguous when a car approaches an intersection, or use additional command information to navigate the vehicle but not automated enough.
• Focusing on making the vehicle drive along the given path, we propose a new navigation command that does not require human's participation and a novel model architecture called angle branched network.
• Both the new navigation command and the angle branched network are easy to understand and effective.