Optimal Vehicle Trajectory Planning With Control Constraints and Recursive Implementation for Automated On-Ramp Merging

We propose a framework based on stochastic collocation to solve autonomous vehicle optimal trajectory planning problems with probabilistic uncertainty. We model uncertainty from the location and size of obstacles. We develop stochastic pseudospectral methods to solve the minimum expectation cost of differential equation, which meets path, control, and boundary constraints. Results are shown on two examples of autonomous vehicle trajectory planning under uncertainty, which illustrated the feasibility and applicability of our method.

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Multi-Car Convex Feasible Set Algorithm in Trajectory Planning

Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions ◽

10.1115/dscc2020-3201 ◽

2020 ◽

Author(s):

Jing Huang ◽

Changliu Liu

Keyword(s):

Convex Optimization ◽

Quadratic Programming ◽

Real Time ◽

Trajectory Planning ◽

Autonomous Driving ◽

Curse Of Dimensionality ◽

Planning Problem ◽

Feasible Set ◽

Planning Algorithm ◽

Vehicle Trajectory

Abstract Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.

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Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410019844434 ◽

2019 ◽

Vol 233 (16) ◽

pp. 6032-6043 ◽

Cited By ~ 2

Author(s):

Jun Tang ◽

Jiayi Sun ◽

Cong Lu ◽

Songyang Lao

Keyword(s):

Unmanned Aerial Vehicles ◽

Collision Avoidance ◽

Unmanned Aerial Vehicle ◽

Trajectory Planning ◽

Potential Field ◽

Three Dimensional ◽

Artificial Potential Field ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Vehicle Trajectory

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.

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Obstacle Avoidance for Unmanned Aerial Vehicle Trajectory Planning Using Virtual Convex Projection Technique

Journal of Guidance Control and Dynamics ◽

10.2514/1.g006100 ◽

2021 ◽

pp. 1-12

Author(s):

Zichen Zhao ◽

Haibin Shang ◽

Yue Dong

Keyword(s):

Obstacle Avoidance ◽

Unmanned Aerial Vehicle ◽

Trajectory Planning ◽

Projection Technique ◽

Aerial Vehicle ◽

Vehicle Trajectory

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A non-cooperative vehicle-to-vehicle trajectory-planning algorithm with consideration of driver’s characteristics

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407018783394 ◽

2018 ◽

Vol 233 (10) ◽

pp. 2405-2420 ◽

Cited By ~ 3

Author(s):

Kuoran Zhang ◽

Jinxiang Wang ◽

Nan Chen ◽

Guodong Yin

Keyword(s):

Trajectory Planning ◽

Vehicle Dynamics ◽

Cooperative Control ◽

Control Algorithm ◽

System A ◽

Vehicle To Vehicle ◽

Vehicle System ◽

Planning Algorithm ◽

Vehicle Trajectory ◽

Driver’S Characteristics

This paper presents a non-cooperative vehicle-to-vehicle trajectory-planning algorithm with consideration of the characteristics of different drivers. The driver–vehicle model considering vehicle dynamics and characteristics of the drivers is used to formulate a vehicle-to-vehicle encountering system. A non-cooperative control algorithm considering each of the driver–vehicle system as a player is employed to plan collision-free trajectories for the encountering vehicles with respective initial driving intentions. The non-cooperative problem is solved with the theory of Nash equilibrium and is ultimately converted to a standard nonlinear Model Predictive Control problem. Simulations are conducted in the scenarios of the lane-exchanging and overtaking with different initial vehicle speeds to verify the effectiveness of the proposed algorithm. Results show that the algorithm can accomplish the trajectory-planning task with consideration of both the safety requirement and the characteristics of drivers. The simulation results also show that the proposed algorithm has effectiveness for trajectory planning in different vehicle-to-vehicle encountering scenarios.

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