TY - GEN
T1 - An efficient decision and planning method for high speed autonomous driving in dynamic environment
AU - Zhang, Kai
AU - Fu, Mengyin
AU - Yang, Yi
AU - Shang, Songtian
AU - Wang, Meiling
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - This paper describes an improved decision and planning algorithm based on our previously proposed methods for unmanned ground vehicle (UGV). The new method can be applied to UGV driving both in structured environment and unstructured environment. In the improved method, the prospect of planning is extended from 40m to 100m for safe driving at high speed and some piecewise linear speed functions are designed for the new prospect. After this improvement our UGV now can drive at a maximum speed of 60km/h rather than 40km/h while avoiding obstacles safely. Besides, a velocity feedforward control is added to make the UGV overtake other cars driving at about 25km/h on the road. At last, the collision detection algorithm is improved to make the lane changing maneuver safer. The proposed decision and planning algorithm is implemented both on our old Polaris all terrain vehicle (ATV) and new FAW-H7 car, which exhibited good performance on Across Dangers & Obstacles 2016, Tahe, China and Future Challenge 2016, Changshu, China, respectively.
AB - This paper describes an improved decision and planning algorithm based on our previously proposed methods for unmanned ground vehicle (UGV). The new method can be applied to UGV driving both in structured environment and unstructured environment. In the improved method, the prospect of planning is extended from 40m to 100m for safe driving at high speed and some piecewise linear speed functions are designed for the new prospect. After this improvement our UGV now can drive at a maximum speed of 60km/h rather than 40km/h while avoiding obstacles safely. Besides, a velocity feedforward control is added to make the UGV overtake other cars driving at about 25km/h on the road. At last, the collision detection algorithm is improved to make the lane changing maneuver safer. The proposed decision and planning algorithm is implemented both on our old Polaris all terrain vehicle (ATV) and new FAW-H7 car, which exhibited good performance on Across Dangers & Obstacles 2016, Tahe, China and Future Challenge 2016, Changshu, China, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85028030883&partnerID=8YFLogxK
U2 - 10.1109/IVS.2017.7995815
DO - 10.1109/IVS.2017.7995815
M3 - Conference contribution
AN - SCOPUS:85028030883
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 806
EP - 811
BT - IV 2017 - 28th IEEE Intelligent Vehicles Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE Intelligent Vehicles Symposium, IV 2017
Y2 - 11 June 2017 through 14 June 2017
ER -