Local Path Planning And Motion Control For Agv In Positioning

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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“…The lateral movement of the vehicle can be modeled with a quintic curve [17], as represented in Equation (17):…”

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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“…Wie bereits in [86] und [92], spielt aus diesem Grund auch nachfolgend die Minimierung des sog. Rucks, die Zeitableitung der Beschleunigung, eine bedeutende Rolle.…”

Section: Abbildung 34: Trajektoriengenerierung In Den Frenet-koordinunclassified

Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

Werling¹

2012

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“…The lower left shaded area depicts a control set for paths in C 0 while the upper right one depicts one designed for higher order continuity, consisting of quintic polynomials. polynomials are attractive for planning dynamic driving manoeuvres, because they minimize squared jerk [21] and allow for fast computation of their coefficients for given boundary conditions. Closed form expressions exist to describe the integral of squared jerk and for maximum speed, acceleration and speed along the trajectory [22].…”

Section: A Spatiotemporal State Latticesmentioning

confidence: 99%

3D perception and planning for self-driving and cooperative automobiles

Stiller

Ziegler

2012

International Multi-Conference on Systems, Sygnals &Amp; Devices

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Abstract-This presentation focusses on key technologies for automobiles that perceive a priori unknown environment and automatically navigate through everyday traffic. Methods for 3D Machine perception based on lidar and video sensors are outlined. Beyond classical metrology, the recognition and basic understanding of situations must be accomplished for automated trajectory planning in urban traffic. We discuss how to represent and acquire metric, symbolic and conceptual knowledge from video and lidar data of a vehicle. A hardware and software architecture tailored to this knowledge structure for an autonomous vehicle is proposed. Emphasis is laid on methods for situation recognition employing geometrical and topological reasoning and Markov Logic Networks. A quality measure for trajectories is imposed that considers safety, efficiency, and comfort. We adopt a flat input parameterization to plan trajectories that optimize the imposed quality measure. Results from the autonomous vehicle AnnieWAY that recently won the Grand Cooperative Driving Challenge are shown in real world urban and platooning scenarios.

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Local Path Planning And Motion Control For Agv In Positioning

Cited by 59 publications

References 2 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

Ein neues Konzept für die Trajektoriengenerierung und -stabilisierung in zeitkritischen Verkehrsszenarien

3D perception and planning for self-driving and cooperative automobiles

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