Trajectory Planning in Frenet Frame via Multi-Objective Optimization

Huang, Jianyu; He, Z.C.; Arakawa, Yutaka; Dawton, Billy

doi:10.1109/access.2023.3294713

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…Team RISE made a path planner based on the Frenet-Frame [9], making it possible to keep lanes and avoid obstacles. Frenet-Frame makes the reference line as s-axis and the normal direction of the reference line as d-axis, as you can see in Figure 9.…”

Section: Path Plannermentioning

confidence: 99%

Autonomous Robot Racing Competitions: Truly Multivehicle Autonomous Racing Competitions [Competitions]

Moon,

Kang,

Eom

et al. 2024

IEEE Robot. Automat. Mag.

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Section: Path Plannermentioning

confidence: 99%

Autonomous Robot Racing Competitions: Truly Multivehicle Autonomous Racing Competitions [Competitions]

Moon,

Kang,

Eom

et al. 2024

IEEE Robot. Automat. Mag.

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“…However, most often in this case, there is a need for separating the planning stage into sequential phases of determining the trajectory and distributing the speed mode. Another approach allows for the consideration of the formation of AV motion in Frenet coordinates [4,5]. In this case, usually, the midline of a road lane is in some way expressed as a reference path function.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

Diachuk,

Easa

2024

Applied Sciences

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The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization. The method includes representing the trajectory by a finite element (FE), determining trajectory parameters in Frenet coordinates, composing a model of vehicle kinematics, defining optimization criteria and a cost function, forming a set of constraints, and adapting the Gaussian N-point scheme for quadrature numerical integration. The study also defines a set of minimum optimization parameters sufficient for making motion predictions with smooth functions of the trajectory and speed. For this, piecewise functions with three degrees of freedom (DOF) in FE’s nodes are implemented. Therefore, the high differentiability of the trajectory and speed functions is ensured to obtain motion criteria such as linear and angular speeds, acceleration, and jerks used in the cost function and constraints. To form the AV roadway position, the Frenet coordinate system and two variable parameters are used: the reference path length and the lateral displacement perpendicular to reference line’s tangent. The trajectory shape, then, depends only on the final position of the AV’s mass center and the final reference’s curvature. The method uses geometric, kinematic, dynamic, and physical constraints, some of which are related to hard restrictions and some to soft restrictions. The planning technique involves parallel forecasting for several variants of the AV maneuver followed by selecting the one corresponding to a specified criterion. The sequential quadratic programming (SQP) technique is used to find the optimal solution. Graphs of trajectories, speeds, accelerations, jerks, and other parameters are presented based on the simulation results. Finally, the efficiency, rapidity, and prognosis quality are evaluated.

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An Adaptive Path Planning Method for Curved Roads via Three-Dimensional Space-Aware Profiling Maps

Huang,

Dawton,

Arakawa

2024

IEEE Access

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In modern transportation, autonomous driving technology has emerged as a significant innovation, offering improved safety, efficiency, and convenience. One of its core technologies is path planning, which creates an optimal route for vehicles in various traffic situations, ensuring they arrive safely and efficiently. However, existing path planning methods suffer from inflexible and inefficient road sampling, and limitations in path expression due to curve models. In this paper, we introduce a novel path planning framework that operates from a coarse-to-optimal approach. We first introduce an adaptive sampling method to construct a threedimensional (3D) space-aware profiling map in the Frenet frame to quantify abstract traffic scenarios. This cost map guides the generation of waypoints by incorporating obstacle distributions in the s-direction and cost values in the l-direction. We then design a cost function to obtain the coarse path by considering ride comfort, anti-deviation from the center of the road, and path safety. Finally, a new path model is re-established based on the Taylor series, and a quadratic programming approach is employed to fine-tune the coarse path to attain the optimal path. Experimental results show that the proposed adaptive sampling method can dynamically adjust the sampling strategy according to the change in the number and distribution of obstacles on the road, significantly improving the sampling effectiveness rate to 100 %, 99.85 %, 99.68 % and 99.61 % for oneobstacle, two-obstacle, three-obstacle, and four-obstacle scenarios. Compared to the conventional method, the generated path length is shortened by 3.48 % and 8.42 % and the maximum heading angle is reduced by 18.68 % and 30.21 % in the scenarios with obstacles of the same and different sizes. INDEX TERMS Path planning, frenet frame, cost function, taylor series, quadratic program, autonomous driving I. INTRODUCTION

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Trajectory Planning in Frenet Frame via Multi-Objective Optimization

Cited by 6 publications

References 44 publications

Autonomous Robot Racing Competitions: Truly Multivehicle Autonomous Racing Competitions [Competitions]

Autonomous Robot Racing Competitions: Truly Multivehicle Autonomous Racing Competitions [Competitions]

Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

An Adaptive Path Planning Method for Curved Roads via Three-Dimensional Space-Aware Profiling Maps

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