Path Planning for Highly Automated Driving on Embedded GPUs

Fickenscher, Jörg; Schmidt, Sandra; Hannig, Frank; Bouzouraa, Mohamed Essayed; Teich, Jürgen

doi:10.3390/jlpea8040035

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…Let us consider the AV motion in the presence of other vehicles (Figure 3). We assume, for instance, that AV is moving along the middle (2) lane and may perform three maneuver variants [21]: lane changes to the first (1) and third (3) lanes and following the second lane. In terms of geometric sense, such a rearrangement may be considered the best, which ensures keeping the greatest distance from an obstacle.…”

Section: Motion-planning Conceptmentioning

confidence: 99%

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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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Section: Motion-planning Conceptmentioning

confidence: 99%

“…Physical constraints. Subject to a relatively equal distribution of longitudinal and transverse forces between the AV wheels, the critical speed V ζs criterion may be used stipulated by limiting the stable motion depending on the trajectory's curvature [21] and tire-road adhesion. It can be derived as follows…”

Section: Constraintsmentioning

confidence: 99%

Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

Diachuk,

Easa

2024

Applied Sciences

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“…We have utilized the approach introduced in reference [16] to estimate the Frenet states from Cartesian values by dividing the global path into smaller segments and locally approximating the divergence from the route.…”

Section: B Local Planningmentioning

confidence: 99%

An Autonomous Driving Framework for Long-term Decision-making and Short-term Trajectory Planning on Frenet Space

Elkaim¹

2020

Preprint

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In this paper, we present a hierarchical framework for decision-making and planning on highway driving tasks. We utilized intelligent driving models (IDM and MOBIL) to generate long-term decisions based on the traffic situation flowing around the ego. The decisions both maximize ego performance while respecting other vehicles' objectives. Short-term trajectory optimization is performed on the Frenet space to make the calculations invariant to the road's three-dimensional curvatures. A novel obstacle avoidance approach is introduced on the Frenet frame for the moving obstacles. The optimization explores the driving corridors to generate spatiotemporal polynomial trajectories to navigate through the traffic safely and obey the BP commands. The framework also introduces a heuristic supervisor that identifies unexpected situations and recalculates each module in case of a potential emergency. Experiments in CARLA simulation have shown the potential and the scalability of the framework in implementing various driving styles that match human behavior.

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“…In this way, the trajectory planning algorithm only need to follow the given reference curve and does not require the information of map like signs, turns and so on. To achieve optimality and time efficiency, many trajectory planning algorithms [2]- [4] are developed in Frenet frames with time to reduce the planning dimension with the help of a reference curve. In a Frenet frame, finding the optimal trajectory is essentially a three dimensional constrained optimization problem.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning

Sun¹,

Ren²,

Lian³

et al. 2020

Preprint

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Trajectory planning is a fundamental task on various autonomous driving platforms, such as social robotics and self-driving cars. Many trajectory planning algorithms use a reference curve based Frenet frame with time to reduce the planning dimension. However, there is a common implicit assumption in classic trajectory planning approaches, which is that the generated trajectory should follow the reference curve continuously. This assumption is not always true in real applications and it might cause some undesired issues in planning. One issue is that the projection of the planned trajectory onto the reference curve maybe discontinuous. Then, some segments on the reference curve are not the image of any part of the planned path. Another issue is that the planned path might self-intersect when following a simple reference curve continuously. The generated trajectories are unnatural and suboptimal ones when these issues happen. In this paper, we firstly demonstrate these issues and then introduce an efficient trajectory generation method which uses a new transformation from the Cartesian frame to Frenet frames. Experimental results on a simulated street scenario demonstrated the effectiveness of the proposed method.

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Path Planning for Highly Automated Driving on Embedded GPUs

Cited by 13 publications

References 21 publications

Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

Simultaneous Trajectory and Speed Planning for Autonomous Vehicles Considering Maneuver Variants

An Autonomous Driving Framework for Long-term Decision-making and Short-term Trajectory Planning on Frenet Space

An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning

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