TY  - JOUR
PY  - 2023//
TI  - Obstacle avoidance maneuver by optimal feedback control using deep learning neural networks
JO  - Transactions of Society of Automotive Engineers of Japan
A1  - Sago, Takashi
A1  - Arai, Yoshihide
A1  - Ueyama, Yuki
A1  - Harada, Masanori
SP  - 1281
EP  - 1286
VL  - 54
IS  - 6
N2  - This paper investigates real-time optimal feedback control for an autonomous vehicle. The applicability of a deep learning neural network controller using the simplified model open-loop optimal control solution as supervised learning data is investigated for path following and obstacle avoidance maneuvers on the road, including straight and curved sections. The constructed controller can obtain optimal control variables for given states and constraints in real-time without iterative computation. The numerical results using the full vehicle model show that the proposed controller has the potential for real-time optimal obstacle avoidance control of the conventional type of vehicle.<p />  <p>Language: ja</p>
LA  - ja
SN  - 0287-8321
UR  - http://dx.doi.org/10.11351/jsaeronbun.54.1281
ID  - ref1
ER  -