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Technical Paper

Toward High Automatic Driving by a Dynamic Optimal Trajectory Planning Method Based on High-Order Polynomials

2020-04-14
2020-01-0106
This paper intends to present a novel optimal trajectory planning method for obstacle avoidance on highways. Firstly, a mapping from the road Cartesian coordinate system to the road Frenet-based coordinate system is built, and the path lateral offset in the road Frenet-based coordinate system is represented by a function of quintic polynomial respecting the traveled distance along the road centerline. With different terminal conditions regarding its position, heading and curvature of the endpoint, and together with initial conditions of the starting point, the path planner generates a bunch of candidate paths via solving nonlinear equation sets numerically. A path selecting mechanism is further built which considers a normalized weighted sum of the path length, curvature, consistency with the previous path, as well as the road hazard risk.
Journal Article

Optimal Cooperative Path Planning Considering Driving Intention for Shared Control

2020-04-14
2020-01-0111
This paper presents an optimal cooperative path planning method considering driver’s driving intention for shared control to address target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial cooperative path profiles of the cooperative path in the Frenet-based coordinate system are induced by considering the driver’s lane-changing intention recognized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization techniques. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach.
Technical Paper

A Switched MPC Lateral Steering Controller Which Considered Tracking Quality and Handling Quality for Autonomous Vehicle

2017-03-28
2017-01-1591
Generally speaking, lateral steering control method which ensures a good performance in tracking quality and handle quality simultaneously for autonomous vehicle is a changeling task. In order to keep the vehicle to stay safe when facing with severe situations such as an emergency lane change, a switched MPC lateral steering controller, which is on the basis of the stability feature of the vehicle, is presented in this paper. First, a MPC steering controller based on the 3DOF nonlinear vehicle model is derived, a comparative study of different vehicle models for MPC prediction are made. It proves that the presented MPC controller based on 3DOF nonlinear vehicle model possesses an advantage of balancing the conflicts between the tracking quality and handling quality of the vehicle.
Journal Article

Influencing Factors Research on Vehicle Path Planning Based on Elastic Bands for Collision Avoidance

2012-09-24
2012-01-2015
This paper presents the different influence factors to vehicle's path planning, including the guide-potential shape and its parameters, the guild-potential influence scale factor, the stiffness of the elastic bands and the speed of the host vehicle. The assessment of emergency path is based on the dynamic performance of the host vehicle, the lateral acceleration and yaw rate, and its mean-square values accesses the stability of the host vehicle when following the path. In order to take evasion maneuvers more steadily, a guide-potential affecting the moving vehicles behind the obstacle is built, which encourages the host vehicle to change lane appropriately. Three different shape guide-potential models, namely half-circle-like, half-ellipse-like and parabola-like, are proposed and compared in this paper. Meanwhile, hazard map of the road environment which includes the lanes, borders and obstacles is generated.
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