TY  - JOUR
PY  - 2021//
TI  - End-to-end learning-based driving system with branches by emphasizing target direction
JO  - Transactions of Society of Automotive Engineers of Japan
A1  - Seiya, Shunya
A1  - Ohtani, Kento
A1  - Carballo, Alexander
A1  - Takeuchi, Eijiro
A1  - Takeda, Kazuya
SP  - 1368
EP  - 1374
VL  - 52
IS  - 6
N2  - End-to-end driving refers to deep learning methods for generating control signals directly from external sensors. Previous methods use a direction vector towards the target to select and turn at intersections. However, the vector has a smaller dimension than the image, and thus it is ignored during training. In this study, we propose a learning method to emphasize that vector by using L2 regularization, which enables a robot to follow trajectories with branches. We validate the system's performance by conducting experiments using several driving scenarios. Our approach allowed an autonomous robot to successfully follow trajectories, including unknown outdoor trajectories.<p />  <p>Language: ja</p>
LA  - ja
SN  - 0287-8321
UR  - http://dx.doi.org/10.11351/jsaeronbun.52.1368
ID  - ref1
ER  -