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Efficient Trajectory Planning for Autonomous Vehicles Using Quadratic Programming With Weak Duality
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AI
DOI：10.1109/TIV.2023.3315387.png)
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Abstract
En 中文
Highly autonomous driving technology is expected to improve driving safety and convenience, and collision avoidance technology is essential for fully autonomous driving. Planning a collision-free trajectory that includes velocity and path is one of the most challenging objectives. Optimization-based trajectory planners have been proposed in many previous studies because they offer a high degree of freedom and can handle various situations. However, most previous trajectory planners used nonlinear programming due to the nonlinearity or non-convexity of the optimization problem. These methods come with a high computational load. The trajectory planner requires the real-time ability to cope with dynamically changing environments. This article focuses on the trajectory planning of autonomous vehicles through quadratic programming (QP), which requires a low computational load. To achieve this, we introduce the longitudinal-lateral decomposition method. In addition, collision-free constraints are expressed as linear constraints through proposed ingenious dual functions. The proposed weak duality optimization problem has a QP form and optimized trajectory and obstacle avoidance timing through only one QP problem. This study verified that the proposed trajectory planner could plan smooth collision-free maneuvers for several driving situations by simulations.
Keywords:
Trajectory planning
Trajectory
Collision avoidance
Computational modeling
Timing
Safety
Autonomous vehicles
Autonomous vehicle
model predictive control (MPC)
quadratic programming (QP)
trajectory planning
weak duality
Journal
I
IF:
14.3
Papers:
1.2K
Citations:
1.2W