Optimal trajectory generation for dynamic street scenarios in a Fren&amp;#x00E9;t Frame

Werling, Moritz; Ziegler, Julius; Kammel, Sören; Thrun, Sebastian

doi:10.1109/robot.2010.5509799

Cited by 615 publications

(402 citation statements)

References 16 publications

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“…Then, an appropriate trajectory is selected by considering comfort and duration. The Frenet frame [16] The growth rate of the accuracy decreases, and it is limited when n is larger than five. The accuracy reaches 93.5% when n is five.…”

Section: Trajectory Prediction With Intent and Motion Modelmentioning

confidence: 99%

“…Then, an appropriate trajectory is selected by considering comfort and duration. The Frenet frame [16] along the lane center line is used to predict the trajectories and then converted to the initial Cartesian coordinates. The lateral component is represented with d(t), and the longitudinal component is s(t).…”

Section: Trajectory Prediction With Intent and Motion Modelmentioning

confidence: 99%

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Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

et al. 2017

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Existing collision avoidance methods for autonomous vehicles, which ignore the driving intent of detected vehicles, thus, cannot satisfy the requirements for autonomous driving in urban environments because of their high false detection rates of collisions with vehicles on winding roads and the missed detection rate of collisions with maneuvering vehicles. This study introduces an intent-estimation-and motion-model-based (IEMMB) method to address these disadvantages. First, a state vector is constructed by combining the road structure and the moving state of detected vehicles. A Gaussian mixture model is used to learn the maneuvering patterns of vehicles from collected data, and the patterns are used to estimate the driving intent of the detected vehicles. Then, a desirable long-term trajectory is obtained by weighting time and comfort. The long-term trajectory and the short-term trajectory, which are predicted using a constant yaw rate motion model, are fused to achieve an accurate trajectory. Finally, considering the moving state of the autonomous vehicle, collisions can be detected and avoided. Experiments have shown that the intent estimation method performed well, achieving an accuracy of 91.7% on straight roads and an accuracy of 90.5% on winding roads, which is much higher than that achieved by the method that ignores the road structure. The average collision detection distance is increased by more than 8 m. In addition, the maximum yaw rate and acceleration during an evasive maneuver are decreased, indicating an improvement in the driving comfort.

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Section: Trajectory Prediction With Intent and Motion Modelmentioning

confidence: 99%

Section: Trajectory Prediction With Intent and Motion Modelmentioning

confidence: 99%

Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

et al. 2017

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“…State space sampling method [3,4] and Frenet Frame based semi-reactive technique [5] is used to generate feasible trajectories on-line. They are both based on predictive control approach.…”

Section: Introductionmentioning

confidence: 99%

“…Commonly, the performance of motion planner mainly depends on trajectory selection; however how to select the preferred trajectory is not mentioned clearly in above researches. In [5] the optimal trajectory was determined by utilizing optimal control approach, but the cost function was different in different behaviors which might make parameters tuning to be complex and superfluous.…”

Section: Introductionmentioning

confidence: 99%

A Motion Planning Method for Autonomous Vehicles

Zhao¹,

Liu²,

Zhu³

et al. 2017

dtetr

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Abstract.A model based predictive motion planning with combining Bayesian Methodology for autonomous driving is proposed. The planning algorithm firstly generates candidate obstacle free trajectories among the current state and sampled desired states using trajectory generation techniques. Then Bayesian Methodology is applied to select the optimal trajectory among candidate trajectories. The preferred trajectory selected in the previous planning cycle is regarded as the prior knowledge, while the trajectory cost in current cycle is transformed into likelihood function. Both the distribution follows the normal distribution and the Bayesian theorem is adopted to calculate the posterior knowledge to determine the preferred trajectory. Then the speed profile is calculated with the preferred trajectory to produce the real trajectory. The proposed motion planner is implemented and tested in simulations. Experiments results show that the planner has good performance in autonomous driving and especially reduces indecision behavior in uncertain environments, and improves stability of autonomous driving. IntroductionThe motion planning is a fundamental technology in autonomous driving. The high performance of motion planner will improve greatly the performance of autonomous driving, especially stability, safety, smoothness, and speed. Because of the inherent dynamic-mechanism delay and hysteresis in autonomous systems, predictive approach [1, 2] is commonly utilized in motion planning. In this approach, motion planning has two key procedures: 1) trajectory generation, and 2) trajectory selection. The former generates motion planning search space, while the latter determines or selects the optimal or preferred trajectory.State space sampling method [3,4] and Frenet Frame based semi-reactive technique [5] is used to generate feasible trajectories on-line. They are both based on predictive control approach. Commonly, the performance of motion planner mainly depends on trajectory selection; however how to select the preferred trajectory is not mentioned clearly in above researches. In [5] the optimal trajectory was determined by utilizing optimal control approach, but the cost function was different in different behaviors which might make parameters tuning to be complex and superfluous.In this paper, an efficient motion planner combining trajectory generation using quintic polynomials with trajectory selection adopting Bayesian method is proposed. The planner has the following features: generated trajectories are satisfying differential, dynamic and road constraints, therefore are kinematically feasible, and are smooth and continuous enough due to high order polynomials; Bayesian based trajectory evaluation and selection deals with the indecision and unstable problem in planning, and guarantees time consistency with sequence knowledge which is formulated as probabilistic principles.

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“…In this way, a global G 1 path is achieved, but with low continuity error in curvature ensuring low lateral acceleration changes. Optimization techniques are also used by Montemerlo et al (2008) and Werling et al (2010). A quintic polynomial optimal path is computed by minimizing a cost function related to lateral jerk.…”

Section: Dynamic Pathsmentioning

confidence: 99%

Piecewise linear continuous-curvature path planning for autonomous vehicles

Silva¹

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2018Trata-se da versão corrigida da dissertação. A versão original se encontra disponível na EESC/USP que aloja o Programa de Pós-Graduação de Engenharia Elétrica. Autonomous vehicles have increasingly become an attractive field due its promising capabilities of improvements regarding safety, comfort, traffic flow etc. A required attribute for those vehicles is the ability of autonomously compute its path towards a destination point. The path must be planned considering the constructive aspects of the vehicle in order to guarantee the maneuver feasibility. This work consists on computing a feasible path for autonomous vehicles with non-holonomic constraints. Piecewise linear continuouscurvature paths constituted of clothoids, circular arcs, and straight lines are used for this purpose, providing passenger's comfort. The road network is modeled from GPS (Global Positioning System) vehicle trajectories by defining lanes, roundabouts and intersections. GPS points are used later to parameterize lanes using clothoids and to extract roundabout centers and radii. This approach provides a sparse road network model since GPS points are replaced by parameterized curves. The information about connections between roads coming from the model is used by a global path planner, which computes a minimal length route from the vehicle current position to the destination point. After that, path planners compute intersection and roundabout paths depending on the nature of connections between roads. Also, lanes changes are performed to obey traffic rules. These three path planners that connects adjacent roads use clothoids, circular arc, and straight lines as interpolating curves whose curvature is constrained to that the vehicle can perform: the intersection path planner uses only a minimal amount of steering to perform the maneuver, increasing the comfort level; the roundabout path planner takes the roundabout center and radius as well as parameters that defines the entrance and exit maneuvers to compute the path; the lane change path planner connects lanes belonging to the same road with a prescribed longitudinal traveled distance depending on whether this maneuver is required. In the end, an global continuous-curvature path is generated. As the result of this work, a real urban scenario is modeled and the proposed approaches are validated. Veículos autônomos têm cada vez mais se tornado um campo atraente de pesquisa devido às suas capacidades promissoras de melhorias em segurança, conforto, fluxo de tráfego, etc. Um atributo necessário para esses veículos é a capacidade de calcular, de forma autônoma, o seu caminho para um ponto de destino. O percurso deve ser planejado considerando os aspectos construtivos do veículo para que a viabilidade das manobras a serem executadas seja garantida. Este trabalho consiste no planejamento de trajetória para veículos autônomos com restrições não-holonômicas. Utilizam-se, para esse efeito, trajetórias cuja curvatura seja contínua e linear por partes, constituídas por clotóides, arcos de circun...

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Optimal trajectory generation for dynamic street scenarios in a Frenét Frame

Cited by 615 publications

References 16 publications

Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments

A Motion Planning Method for Autonomous Vehicles

Piecewise linear continuous-curvature path planning for autonomous vehicles

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