Local path planning algorithm for autonomous vehicle based on multi-objective trajectory optimization in state lattice

4 January 2021 Local path planning algorithm for autonomous vehicle based on multi-objective trajectory optimization in state lattice

Ivan I. Kornev, Vladislav I. Kibalov, Oleg Shipitko

Author Affiliations +

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051I (2021) https://doi.org/10.1117/12.2587614
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

The paper presents an algorithm for constructing a local path for a vehicle with nonholonomic kinematics of an automobile type. A local path is a sequence of transitions in the graph of possible maneuvers that minimizes a given cost function. The graph is constructed by duplicating along the global path pre-calculated in a curvilinear coordinate system set of kinematically feasible motion primitives. The use of pre-computed motion primitives significantly reduces the time of graph construction. The weight of each maneuver – the edge of the transition graph – is calculated as a weighted sum of costs based on several criteria. The specified cost function minimizes maneuvering and maintains a safe distance to static obstacles. The information about obstacles is extracted from an occupancy grid map. Dijkstra‘s algorithm is used to search a path in the weighted directed graph. The algorithm was tested on a dataset containing real road scenes. Each scene represents a given global path and a static environment model where a safe local path must be found. Local path search is performed in real-time. Experiments have shown that safe local paths have been found in all scenes where it was physically possible. At the same time, the obtained local paths were on average only on 1:3% longer than the given global paths which demonstrate the high applicability of the proposed algorithm.

Citation Download Citation

Ivan I. Kornev, Vladislav I. Kibalov, and Oleg Shipitko "Local path planning algorithm for autonomous vehicle based on multi-objective trajectory optimization in state lattice", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051I (4 January 2021); https://doi.org/10.1117/12.2587614

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available