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000838762 0247_ $$a10.1007/978-3-658-21954-3$$2doi
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000838762 1001_ $$aHeinrich, Steffen,$$eauthor.
000838762 24510 $$aPlanning universal on-road driving strategies for automated vehicles /$$cSteffen Heinrich.
000838762 264_1 $$aWiesbaden, Germany :$$bSpringer,$$c2018.
000838762 300__ $$a1 online resource (xv, 133 pages) :$$billustrations.
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000838762 4901_ $$aAutoUni -- Schriftenreihe ;$$vBand 119
000838762 504__ $$aIncludes bibliographical references.
000838762 5050_ $$aIntro; Acknowledgements; Table of contents; List of Figures; List of Tables; Summary; Zusammenfassung; 1 Introduction, motivation and structure of the thesis; 1.1 Motivation of the thesis; 1.2 Thesis research questions; 1.3 Thesis contributions; 1.4 Thesis outline; 2 Preliminaries; 2.1 Introduction to motion planning; 2.2 Terminology; 2.3 Taxonomy of planning methods; 2.4 Motion planning for automated cars; 2.4.1 Diversity of driving environments; 2.4.2 Planning assessment criteria and driving modes; 2.5 Problem statement; 3 Related work
000838762 5058_ $$a3.1 From advanced driver assistance systems towards automated cars3.1.1 Trajectory planning; 3.1.2 On-road swerve path generation; 3.1.3 Collision checking and avoidance; 3.2 Optimization methods; 3.3 Driving mode selection; 3.3.1 Multi-layered search space representations; 3.3.2 Hierarchical driving mode state machine; 3.3.3 End-to-end machine learning approaches for autonomous driving applications; 4 A framework for universal driving strategy planning; 4.1 Planning in high dimensional state space; 4.1.1 Identifying key components of sampling based planning
000838762 5058_ $$a4.1.2 PSP integration into existing architecture4.1.3 Challenges and opportunities: Modeling a universal drive; 4.1.4 Non-functional requirements: safety, comfort and acceptance; 4.2 PSP world representations; 4.3 Visualizing high dimensional solutions; 5 Sampling-based planning in phase space; 5.1 Schematic of a complete planning sequence; 5.2 State space setup and exploration; 5.2.1 State propagation strategies; 5.2.2 Vehicle motion guided sampling; 5.2.3 Random state sampling; 5.3 Trajectory planning; 5.3.1 Generating path segments with clothoids; 5.3.2 Generating velocity profiles
000838762 5058_ $$a5.4 Trajectory optimization5.4.1 Extended planning horizon with multiple layers; 5.4.2 Directed graph optimization using dynamic programming; 5.4.3 Other PSP optimization methods; 5.5 Rules and heuristics; 5.5.1 Simplification of the state space; 5.5.2 How to choose a planning horizon length?; 6 A universal approach for driving strategies; 6.1 Modeling driving behaviors: Cost functions; 6.1.1 State transition costs; 6.1.2 Optimizing for a universal driving experience; 6.2 Situation awareness: Observing the most relevant things; 6.2.1 Optimizing the vehicle's sensor coverage
000838762 5058_ $$a6.2.2 Interpretation of a sensor coverage cost map6.3 Simulation experiments; 6.3.1 Validation of driving strategy generation; 6.3.2 Evaluation of smart positioning; 6.3.3 PSP performance analysis; 7 Modeling ego motion uncertainty; 7.1 Why modeling uncertainty matters; 7.2 Ego motion uncertainty; 7.2.1 A post-processing planning extension; 7.2.2 State estimation and model correction; 7.2.3 Re-evaluate trajectory collision probability; 7.2.4 Collision detection algorithm; 7.3 Simulation experiments; 8 Summary, outlook and contributions; References; A Supplemental material
000838762 506__ $$aAccess limited to authorized users.
000838762 520__ $$aSteffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account. Contents A Framework for Universal Driving Strategy Planning Sampling-Based Planning in Phase Space A Universal Approach for Driving Strategies Modeling Ego Motion Uncertainty Target Groups Scientists and students in the field of robotics, computer science, mechanical engineering Engineers in the field of vehicle automation, intelligent systems and robotics About the Author Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.
000838762 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 24, 2018).
000838762 650_0 $$aAutomated vehicles.
000838762 650_0 $$aDriver assistance systems.
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