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

Werling, Moritz

doi:10.1524/auto.2012.0970

Cited by 15 publications

(25 citation statements)

References 39 publications

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Section: Introductionmentioning

confidence: 99%

“…Early results [12] used linear tire models, but approaches with more realistic, nonlinear tire models are known [24]. The tracking error can be expressed in earth-fixed coordinates [21] or trajectory-fixed coordinates [24] and may be compensated by an inversion-based nonlinear mapping of a linear feedback-controller. A subset of the input/output linearizable systems is full-state linearizable, which are known as flat systems [6], possessing the beneficial property of a possible static mapping from the desired trajectory to the full vehicle state and input vector [8].…”

Section: Introductionmentioning

confidence: 99%

“…Among them is a worstcase deviation obtained by rapidly exploring random trees (RRTs), a mean deviation obtained by Monte-Carlo simulation, and a parameter sensitivity analysis. The second contribution is a comparison of inversion-based controllers based on the work in [24] and [7] using the proposed benchmarks. All details of the benchmark problems are fully available for download 1 .…”

Section: Introductionmentioning

confidence: 99%

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Comparison of trajectory tracking controllers for emergency situations

Hess

Althoff

Sattel

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Over the last years a number of different vehicle controllers has been proposed for tracking planned paths or trajectories. Most of previously published works do not compare their results with other approaches or limit the comparison to a few scenarios. Unfortunately, comparisons with existing controller concepts are very rare and a ranking is hard to establish from the literature. In this work, we rigorously compare inversion-based trajectory tracking controllers by systematically exploring the set of possible solutions when disturbances vary over time and initial states and parameters are uncertain. By using Monte-Carlo simulation, we determine the average performance and by using rapidly exploring random trees, we determine the worst-case performance, which is especially important in emergency situations when avoiding a crash is essential. The tested scenarios and the applied methodologies are documented in detail so that they serve as benchmark problems for other control concepts. The results show that the controller with smaller relative degree performs better with respect to the worst-case deviation computed by rapidly exploring random trees, while conventional simulations of random scenarios would not reveal any difference.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of trajectory tracking controllers for emergency situations

Hess

Althoff

Sattel

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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“…The lateral controller is based on the control concept discussed in [41]. Since the lateral controller should be used up to a moderate dynamic range, a simplified vehicle model (e.g.…”

Section: ) Lateral Dynamics Controlmentioning

confidence: 99%

Experience, Results and Lessons Learned from Automated Driving on Germany's Highways

Aeberhard

Rauch

Bahram

et al. 2015

IEEE Intell. Transport. Syst. Mag.

210

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The BMW Group Research and Technology has been testing automated vehicles on Germany's highways since Spring 2011. Since then, thousands of kilometers have been driven on the highways around Munich, Germany. Throughout this project, fundamental technologies, such as environment perception, localization, driving strategy and vehicle control, were developed in order to safely operate prototype automated vehicles in real traffic with speeds up to 130 km/h. The goal of this project was to learn what technologies are necessary for automated driving. This paper presents the architecture and algorithms developed during this project, results from real driving scenarios, the lessons learned throughout the project and a quick introduction into the latest developments for improving the system.

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“…Jerks compromize the comfort for the passengers and may also excite oscillations in the control system. The trajectory generation method described in [10] and [11] avoids jerks. The method is based on quintic polynoms, because the first and second derivative of a quintic polynomial are continuous.…”

Section: Controller Evaluationmentioning

confidence: 99%

Automated driving, technical approach with motion control architecture

Raste

Lüke

Eckert

2015

At - Automatisierungstechnik

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Automated Driving is the key technology for greater safety, comfort and efficiency in personal transportation. Motion Control ensures that the high-level behavior planning is carried out properly even in case of failures and disturbances. Based on the flatness property a feedforward trajectory control is obtained to ensure lateral motion along prescribed trajectories. An optimal state feedback control reduces the effects of disturbances like side wind or road bank to a minimum.

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

Cited by 15 publications

References 39 publications

Comparison of trajectory tracking controllers for emergency situations

Comparison of trajectory tracking controllers for emergency situations

Experience, Results and Lessons Learned from Automated Driving on Germany's Highways

Automated driving, technical approach with motion control architecture

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