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Implications of Path Tolerance and Path Characteristics on Critical Vehicle Maneuvers
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Path planning and path following are core components in safe autonomous driving. Typically a path planner provides a path with sometolerance on how tightly the path should be followed. Based on that, and other path characteristics, for example sharpness of curves, a speed profile needs to be assigned so that the vehicle can stay within the given tolerance without going unnecessarily slow. Here, such trajectory planning is based on optimal control formulations where critical cases arises as on-the-limit solutions. Several results are obtained on required model complexity depending on path characteristics, for example quantification of required path tolerance for a simple model to be sufficient, quantification of when yaw inertia needs to be considered in more detail, and how the curvature rate of change of curvature interplays with available friction. Overall, in situations where the path planner, due to the type of vehicle operation ranging from good transport roads to more tricky maneuvering, give paths with a wide range of tolerances and characteristics, then the results provided give the basis for real-time path following with reasonably high speed.

National Category
Vehicle Engineering Control Engineering
URN: urn:nbn:se:liu:diva-127797OAI: diva2:927727
Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2016-05-25Bibliographically approved
In thesis
1. Models and Critical Maneuvers for Road Vehicles
Open this publication in new window or tab >>Models and Critical Maneuvers for Road Vehicles
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As manufacturers are pushing their research and development toward more simulation based and computer aided methods, vehicle dynamics modeling and simulation become more important than ever. The challenge lies in how to utilize the new technology to its fullest, delivering the best possible performance given certain objectives and current restrictions. Here, optimization methods in different forms can be a tremendous asset. However, the solution to an optimization problem will always rely on the problem formulation, where model validity plays a crucial role. The main emphasis in this thesis lies within methodology and analysis of optimal control oriented topics for safety-critical road-vehicle maneuvers. A crucial element here is the vehicle models. This is investigated as a first study, evaluating the degree to which different model configurations can represent the lateral vehicle dynamics in critical maneuvers, where it is shown that even the low-complexity models describe the most essential vehicle characteristics surprisingly well.

How to formulate the optimization problems and utilize optimal control tools is not obvious. Therefore, a methodology for road-vehicle maneuvering in safety-critical driving scenarios is presented, and used in evaluation studies of various vehicle model configurations and different road-surface conditions. It was found that the overall dynamics is described similarly for both the high- and low-complexity models, as well as for various road-surface conditions.

If more information about the surroundings is available, the best control actions might differ from the ones in traditional safety systems. This is also studied, where the fundamental control strategies of classic electronic stability control is compared to the optimal strategy in a safety-critical scenario. It is concluded that the optimal braking strategy not only differs from the traditional strategies, but actually counteracts the fundamental intentions from which the traditional systems are based on.

In contrast to passenger cars, heavy trucks experience other characteristics due to the different geometric proportions. Rollover is one example, which has to be considered in critical maneuvering. Model configurations predicting this phenomenon are investigated using optimal control methods. The results show that the simple first go-to models have to be constrained very conservatively to prevent rollover in more rapid maneuvers.

In vehicle systems designed for path following, which has become a trending topic with the expanding area of automated driving, the requirements on vehicle modeling can be very high. These requirements ultimately depend on several various properties, where the path restrictions and path characteristics are very influential factors. The interplay between these path properties and the required model characteristics is here investigated. In situations where a smooth path is obtained, low-complexity models can suffice if path deviation tolerances are set accordingly. In more rapid and tricky maneuvers, however, vehicle properties such as yaw inertia are found to be important.

Several of the included studies indicate that vehicle models of lower complexity can describe the overall dynamics sufficiently in critical driving scenarios, which is a valuable observation for future development.

Place, publisher, year, edition, pages
Linköping University Electronic Press, 2016. 16 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1763
National Category
Control Engineering Vehicle Engineering
urn:nbn:se:liu:diva-127798 (URN)10.3384/diss.diva-127798 (DOI)978-91-7685-771-7 (Print) (ISBN)
Public defence
2016-06-10, Planck, Fysikhuset, Campus Valla, Linköping, 10:15 (English)
VINNOVAeLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research Council
Available from: 2016-05-25 Created: 2016-05-12 Last updated: 2016-05-25Bibliographically approved

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Lundahl, KristofferFrisk, ErikNielsen, Lars
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