Kooperativer Bibliotheksverbund

UID:

Umfang: 1 Online-Ressource

ISBN: 9783031604942

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-60493-5

Weitere Ausg.: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-60496-6

Sprache: Englisch

DOI: 10.1007/978-3-031-60494-2

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: lizenzpflichtig

Mehr zum Autor: Deml, Barbara 1977-

Mehr zum Autor: Flemisch, Frank Ole

Mehr zum Autor: Eckstein, Lutz 1969-

Mehr zum Autor: Althoff, Matthias 1981-

UID:

Umfang: 1 online resource (601 pages)

Ausgabe: 1st ed.

ISBN: 9783031604942

Anmerkung: Intro -- Editorial -- Contents -- Perception and Prediction with Implicit Communication -- How Cyclists' Body Posture Can Support a Cooperative Interaction in Automated Driving -- 1 Introduction -- 1.1 Space-Sharing Conflicts Between Cyclists and AVs in Low-Speed Areas -- 1.2 Recognizing Intentions of Cyclists in Low-Speed Areas -- 1.3 Communication Between Automated Vehicles and Cyclists in Low-Speed Areas -- 1.4 Investigating Space-Sharing Conflicts Between Automated Vehicles and Cyclists -- 1.5 Aims of the Research Project ``KIRa'' -- 2 Investigating the Body Posture as a Predictor for the Starting Process of Cyclists -- 3 Development and Validation of a VR Cycling Simulation -- 3.1 Development of a VR Cycling Simulation -- 3.2 Validation of the VR Cycling Simulation in Terms of Perceived Criticality as Well as Experience of Presence -- 4 Experimental Evaluation of a Drift-Diffusion Model for Vehicle Deceleration Detection -- 5 Investigation of Factors Influencing the Gap Acceptance of Cyclists -- 6 Summary -- 7 Further Reading -- References -- Prediction of Cyclists' Interaction-Aware Trajectory for Cooperative Automated Vehicles -- 1 Introduction -- 2 Related Work -- 2.1 Datasets -- 3 Algorithm -- 3.1 Model Implementation -- 3.2 Data Augmentation -- 4 Evaluation -- 4.1 Evaluation of Different Input Data -- 4.2 Evaluation of Prediction Time Horizons -- 5 Discussion and Future Work -- 6 Conclusion -- References -- Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving -- 1 Introduction -- 1.1 Main Goals -- 1.2 Outline -- 2 Cooperative Perception and Tracking -- 2.1 Context Dependent Detection -- 2.2 Cooperative Detection and Tracking of Cyclists -- 2.3 Pedestrian and Cyclist Tracking Including Class Probabilities -- 2.4 Cooperative Context Determination -- 3 Intention Detection. , 3.1 Basic Movement Detection -- 3.2 Trajectory Forecasting -- 4 Cooperative Intention Detection -- 4.1 Interim Summary of Vehicle, Infrastructure, and Smart Device Based Intention Detection -- 4.2 Cyclists as Additional Sensors -- 4.3 Smart Device Cooperation for Intention Detection -- 4.4 Cooperative Basic Movement Detection -- 4.5 Cooperative Trajectory Forecasting Using the CSGE -- 4.6 Cooperative Probabilistic Trajectory Fusion Using Orthogonal Polynomials -- 5 Prospects -- References -- Analysis and Simulation of Driving Behavior at Inner City Intersections -- 1 Introduction -- 2 Related Work -- 3 Intersection Complexity for Behavior Prediction -- 3.1 Intersection and Behavior Features -- 3.2 Prediction of Driving Behavior -- 4 Behavior Generation -- 4.1 Basic Setup -- 4.2 Decision Making Algorithm -- 4.3 Simulation Results -- 5 Conclusion -- References -- Perception and Prediction with Explicit Communication -- Robust Local and Cooperative Perception Under Varying Environmental Conditions -- 1 Introduction -- 1.1 Concept -- 1.2 Related Work -- 1.3 Data Sets -- 1.4 RESIST Framework and Workflow -- 2 Simulation of Environmental Conditions -- 2.1 Rain -- 2.2 Road Spray -- 2.3 Dust -- 2.4 Snow -- 2.5 Fog -- 3 Evaluation Metrics for Object Perception -- 3.1 Common Metrics for Perception Evaluation -- 3.2 Safety Metric -- 3.3 Comprehensive Safety Metric Score -- 3.4 Data Set Evaluation with the Safety Metric -- 4 Optimization of Object Perception -- 4.1 Influence of Weather on Perception -- 4.2 Optimization of Local Perception -- 4.3 Cooperative Perception -- 5 Conclusion and Outlook -- References -- Design and Evaluation of V2X Communication Protocols for Cooperatively Interacting Automobiles -- 1 Motivation and Technical Background -- 2 V2X Communications-Based Sensor Data Sharing -- 2.1 Overview -- 2.2 State of the Art in Collective Perception. , 2.3 Protocol Design -- 2.4 Adapting Object Filtering to the Available Channel Resources -- 2.5 Redundancy Mitigation Rules -- 2.6 Simulation Results -- 3 V2X Communication-Based Maneuver Coordination -- 3.1 Overview -- 3.2 Protocol Design -- 3.3 Simulation Results -- 4 Summary and Outlook -- References -- Motion Planning -- Interaction-Aware Motion Planningpg as a Game -- 1 Introduction -- 2 Problem Statement -- 3 Bi-level Formulation -- 3.1 NLP of the Follower -- 3.2 NLP of the Leader -- 4 Single-Level Representation -- 4.1 Solving the Complementarity Constraints -- 5 Application to Motion Planning for AVs -- 5.1 Trajectory Optimization for AVs -- 6 Evaluation -- 6.1 Base Scenario -- 6.2 Influence the Human's State -- 6.3 Interaction-Aware Trajectory Optimization -- 6.4 Runtime Experiments -- 7 Algorithm Discussion -- 8 Conclusion -- References -- Designing Maneuver Automata of Motion Primitives for Optimal Cooperative Trajectory Planning -- 1 Introduction -- 2 Models and Symmetry -- 3 Trim Primitives -- 3.1 Choice Based on Road-Geometry -- 3.2 Choice Based on Driving Data -- 4 Maneuvers -- 4.1 Polynomial Approach -- 4.2 Optimal and Pareto-Optimal Maneuvers -- 5 Maneuver Automaton Selection -- 6 Planning Algorithms -- 6.1 Optimized Primitives (normal upper PiΠ*) Search -- 6.2 Reinforcement Learning -- 6.3 Graph-Based Receding Horizon Control -- 6.4 Motion Graphs as Mixed Logical Dynamical System -- 7 Conclusion -- References -- Prioritized Trajectory Planningpg for Networked Vehicles Using Motion Primitives -- 1 Introduction -- 1.1 Motivation -- 1.2 Related Work -- 1.3 Contribution -- 1.4 Notation -- 1.5 Structure -- 2 Receding Horizon Graph Search for Trajectory Planning -- 2.1 Trajectory Planning Problem -- 2.2 Motion Primitive Automaton as System Model -- 2.3 Receding Horizon Graph Search Algorithm -- 2.4 Recursive Agent-Feasibility. , 3 Prioritized Trajectory Planning -- 3.1 Reprioritization Framework for Recursive NCS-Feasibility -- 3.2 Priority Assignment Algorithms -- 4 Numerical and Experimental Results -- 4.1 Cyber-Physical Mobility Lab -- 4.2 Evaluation of Receding Horizon Graph Search -- 4.3 Evaluation of Time-Variant Priority Assignment -- 5 Conclusion -- References -- Maneuver-Level Cooperation of Automated Vehicles -- 1 Introduction -- 2 Framework of Explicitly Negotiated Maneuver Cooperation via V2V -- 2.1 Related Work -- 2.2 Definition of a Cooperative Maneuver -- 2.3 Reservation Templates -- 2.4 Simulations and Driving Experiments -- 2.5 Conclusion -- 3 Cooperation in Emergency Situations -- 3.1 Related Work -- 3.2 Approach -- 3.3 Simulations and Driving Experiments -- 3.4 Conclusion -- 4 Implicitly Cooperative Decision-Making -- 4.1 Related Work -- 4.2 Approach -- 4.3 Experiment -- 4.4 Conclusion -- 5 Conclusion -- References -- Hierarchical Motion Planning for Consistent and Safe Decisions in Cooperative Autonomous Driving -- 1 Introduction -- 1.1 Relevant Work -- 1.2 Contribution -- 2 A Hierarchical Approach to Decision Making -- 3 Group Coordination -- 4 Maneuver Planning -- 4.1 Planning Based on Hybrid Models and Controllable Sets -- 4.2 Illustration for an Overtaking Maneuver -- 5 Trajectory Control -- 6 Conclusions -- References -- Specification-Compliant Motion Planning of Cooperative Vehicles Using Reachable Sets -- 1 Introduction -- 2 Related Work -- 2.1 Specification-Compliant Motion Planning -- 2.2 Cooperative Motion Planning -- 3 Preliminaries -- 3.1 Setup and Coordinate System -- 3.2 System Dynamics -- 3.3 Reachable Set -- 3.4 Propositional Logic -- 4 Computing Specification-Compliant Reachable Sets -- 4.1 State Space Partitioning -- 4.2 Position Predicates -- 4.3 Realizable Sets of Position Predicates -- 4.4 Velocity Predicates. , 4.5 General Traffic Situation Predicates -- 4.6 Computation of Reachable Sets -- 5 Negotiation of Reachable Sets -- 5.1 Problem Statement -- 5.2 Conflict Resolution -- 6 Evaluation -- 6.1 Scenario I: Precise Overtaking -- 6.2 Scenario II: Highway -- 6.3 Scenario III: Roundabout -- 7 Conclusions -- References -- AutoKnigge-Modeling, Evaluation and Verification of Cooperative Interacting Automobiles -- 1 Introduction -- 2 Learning-Based and Vehicle Capability-Aware Architecture for Clustering of Cooperative Interacting Automobiles -- 2.1 Requirements for an Extended LTS Architecture -- 2.2 Extended LTS Architecture -- 2.3 Cooperative Velocity Adaption Algorithm -- 2.4 Learning-Based Clustering -- 2.5 Example Cooperation Intersection and Highway Access -- 2.6 Conclusion and Outlook -- 3 Verification of Cooperative Interacting Automobiles -- 3.1 Introduction -- 3.2 Verification Architecture -- 3.3 Rule Set Generation -- 3.4 Rule Checker -- 3.5 Evaluation -- 3.6 Conclusion -- 4 Modeling Dynamic Systems -- 4.1 Why Modeling? -- 4.2 The EMA Data Type System -- 4.3 Components, Ports, and Connectors -- 4.4 Execution Semantics -- 4.5 MontiMath -- 4.6 Cooperative Agents and EmbeddedMontiArc Dynamics -- 4.7 EMAD Execution Semantics -- 4.8 Conclusion -- 5 Conclusion -- References -- Implicit Cooperative Trajectory Planning with Learned Rewards Under Uncertainty -- 1 Introduction -- 2 Implicit Cooperative Trajectory Planning -- 2.1 Related Work -- 2.2 Problem Formulation -- 2.3 Approach -- 2.4 Experiments -- 3 Learning Reward Functions -- 3.1 Related Work -- 3.2 Problem Formulation -- 3.3 Approach -- 3.4 Experiments -- 4 Planning Under Uncertainties -- 4.1 Related Work -- 4.2 Problem Formulation -- 4.3 Approach -- 4.4 Experiments -- 5 Conclusion -- References -- Learning Cooperative Trajectoriespg at Intersections in Mixed Traffic. , 1 Traffic Signal Controller with Deep Reinforcement Learning.

Weitere Ausg.: Print version: Stiller, Christoph Cooperatively Interacting Vehicles Cham : Springer International Publishing AG,c2024 ISBN 9783031604935

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books.

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UID:

Umfang: 1 online resource (601 pages)

Ausgabe: 1st ed. 2024.

ISBN: 3-031-60494-6

Inhalt: This open access book explores the recent developments automated driving and Car2x-communications are opening up attractive opportunities future mobility. The DFG priority program “Cooperatively Interacting Automobiles” has focused on the scientific foundations for communication-based automated cooperativity in traffic. Communication among traffic participants allows for safe and convenient traffic that will emerge in swarm like flow. This book investigates requirements for a cooperative transport system, motion generation that is safe and effective and yields social acceptance by all road users, as well as appropriate system architectures and robust cooperative cognition. For many years, traffic will not be fully automated, but automated vehicles share their space with manually driven vehicles, two-wheelers, pedestrians, and others. Such a mixed traffic scenario exhibits numerous facets of potential cooperation. Automated vehicles must understand basic principles of human interaction in traffic situations. Methods for the anticipation of human movement as well as methods for generating behavior that can be anticipated by others are required. Explicit maneuver coordination among automated vehicles using Car2X-communications allows generation of safe trajectories within milliseconds, even in safety-critical situations, in which drivers are unable to communicate and react, whereas today's vehicles delete their information after passing through a situation, cooperatively interacting automobiles should aggregate their knowledge in a collective data and information base and make it available to subsequent traffic.

Anmerkung: Part I. Perception and Prediction with Implicit Communication -- Chapter 1. How cyclists’ body posture can support a cooperative interaction in automated driving (Daniel Trommler) -- Chapter 2. Prediction of cyclists' interaction-aware trajectory for cooperative automated vehicles (Dominik Raeck) -- Chapter 3. Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving (DeCoInt2) (Stefan Zernetsch) -- Chapter 4. Analysis and simulation of driving behavior at inner city intersections (Hannes Weinreuter) -- Part II. Perception and Prediction with Explicit Communication -- Chapter 5. Robust Local and Cooperative Perception under Varying Weather Conditions (Jörg Gamerdinger) -- Chapter 6. Design and Evaluation of V2X Communication Protocols for Cooperatively Interacting Automobiles (Quentin Delooz) -- Part III. Motion Planning -- Chapter 7. Interaction-Aware Motion Planning as a Game (Christoph Burger) -- Chapter 8. Designing Maneuver Automata of Motion Primitives for Optimal Cooperative Trajectory Planning (Matheus V. A. Pedrosa) -- Chapter 9. Prioritized Trajectory Planning for Networked Vehicles Using Motion Primitives (Patrick Scheffe) -- Chapter 10. Maneuver-level cooperation of automated vehicles (Matthias Nichting) -- Chapter 11. Hierarchical Motion Planning for Consistent and Safe Decisions in Cooperative Autonomous Driving (Jan Eilbrecht) -- Chapter 12. Specification-Compliant Motion Planning of Cooperative Vehicles Using Reachable Set (Edmond Irani Liu) -- Chapter 13. AutoKnigge - Modeling, Evaluation and Verification of Cooperative Interacting Automobiles (Christian Kehl) -- Chapter 14. Implicit Cooperative Trajectory Planning under Uncertainty with Learned Rewards (Karl Kurzer) -- Chapter 15. Learning Cooperative Trajectories at Intersections in Mixed Traffic via Reinforcement Learning (S. Yan) -- Part IV. Human Factors -- Chapter 16. Cooperative Hub for Cooperative Research on Cooperatively Interacting Vehicles: Use-Cases, Design and Interaction Patterns (Frank Flemisch) -- Chapter 17. Cooperation between Vehicle and Driver: Predicting the Driver’s Takeover Capability in Cooperative Automated Driving based on Orientation Patterns (Nicolas Herzberger) -- Chapter 18. Confidence Horizons: Dynamic Balance of Human and Automation Control Ability in Cooperative Automated Driving (Marcel Usai) -- Chapter 19. Cooperation Behavior of Drivers at Inner City Deadlock-Situations (Nadine-Rebecca Strelau) -- Chapter 20. Measuring and describing cooperation between road users - Results from CoMove (Laura Quante).

Weitere Ausg.: ISBN 3-031-60493-8

Sprache: Englisch

DOI: 10.1007/978-3-031-60494-2

UID:

Umfang: XI, 608 p. 259 illus., 199 illus. in color. , online resource.

Ausgabe: 1st ed. 2024.

ISBN: 9783031604942

Inhalt: This open access book explores the recent developments automated driving and Car2x-communications are opening up attractive opportunities future mobility. The DFG priority program "Cooperatively Interacting Automobiles" has focused on the scientific foundations for communication-based automated cooperativity in traffic. Communication among traffic participants allows for safe and convenient traffic that will emerge in swarm like flow. This book investigates requirements for a cooperative transport system, motion generation that is safe and effective and yields social acceptance by all road users, as well as appropriate system architectures and robust cooperative cognition. For many years, traffic will not be fully automated, but automated vehicles share their space with manually driven vehicles, two-wheelers, pedestrians, and others. Such a mixed traffic scenario exhibits numerous facets of potential cooperation. Automated vehicles must understand basic principles of human interaction in traffic situations. Methods for the anticipation of human movement as well as methods for generating behavior that can be anticipated by others are required. Explicit maneuver coordination among automated vehicles using Car2X-communications allows generation of safe trajectories within milliseconds, even in safety-critical situations, in which drivers are unable to communicate and react, whereas today's vehicles delete their information after passing through a situation, cooperatively interacting automobiles should aggregate their knowledge in a collective data and information base and make it available to subsequent traffic.

Anmerkung: Part I. Perception and Prediction with Implicit Communication -- Chapter 1. How cyclists' body posture can support a cooperative interaction in automated driving (Daniel Trommler) -- Chapter 2. Prediction of cyclists' interaction-aware trajectory for cooperative automated vehicles (Dominik Raeck) -- Chapter 3. Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving (DeCoInt2) (Stefan Zernetsch) -- Chapter 4. Analysis and simulation of driving behavior at inner city intersections (Hannes Weinreuter) -- Part II. Perception and Prediction with Explicit Communication -- Chapter 5. Robust Local and Cooperative Perception under Varying Weather Conditions (Jörg Gamerdinger) -- Chapter 6. Design and Evaluation of V2X Communication Protocols for Cooperatively Interacting Automobiles (Quentin Delooz) -- Part III. Motion Planning -- Chapter 7. Interaction-Aware Motion Planning as a Game (Christoph Burger) -- Chapter 8. Designing Maneuver Automata of Motion Primitives for Optimal Cooperative Trajectory Planning (Matheus V. A. Pedrosa) -- Chapter 9. Prioritized Trajectory Planning for Networked Vehicles Using Motion Primitives (Patrick Scheffe) -- Chapter 10. Maneuver-level cooperation of automated vehicles (Matthias Nichting) -- Chapter 11. Hierarchical Motion Planning for Consistent and Safe Decisions in Cooperative Autonomous Driving (Jan Eilbrecht) -- Chapter 12. Specification-Compliant Motion Planning of Cooperative Vehicles Using Reachable Set (Edmond Irani Liu) -- Chapter 13. AutoKnigge - Modeling, Evaluation and Verification of Cooperative Interacting Automobiles (Christian Kehl) -- Chapter 14. Implicit Cooperative Trajectory Planning under Uncertainty with Learned Rewards (Karl Kurzer) -- Chapter 15. Learning Cooperative Trajectories at Intersections in Mixed Traffic via Reinforcement Learning (S. Yan) -- Part IV. Human Factors -- Chapter 16. Cooperative Hub for Cooperative Research on Cooperatively Interacting Vehicles: Use-Cases, Design and Interaction Patterns (Frank Flemisch) -- Chapter 17. Cooperation between Vehicle and Driver: Predicting the Driver's Takeover Capability in Cooperative Automated Driving based on Orientation Patterns (Nicolas Herzberger) -- Chapter 18. Confidence Horizons: Dynamic Balance of Human and Automation Control Ability in Cooperative Automated Driving (Marcel Usai) -- Chapter 19. Cooperation Behavior of Drivers at Inner City Deadlock-Situations (Nadine-Rebecca Strelau) -- Chapter 20. Measuring and describing cooperation between road users - Results from CoMove (Laura Quante).

In: Springer Nature eBook

Weitere Ausg.: Printed edition: ISBN 9783031604935

Weitere Ausg.: Printed edition: ISBN 9783031604959

Weitere Ausg.: Printed edition: ISBN 9783031604966

Sprache: Englisch

DOI: 10.1007/978-3-031-60494-2

URL: https://doi.org/10.1007/978-3-031-60494-2