UID:
almahu_9949863649402882
Format:
1 online resource (601 pages)
Edition:
1st ed.
ISBN:
9783031604942
Note:
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.
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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.
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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.
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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.
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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.
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1 Traffic Signal Controller with Deep Reinforcement Learning.
Additional Edition:
Print version: Stiller, Christoph Cooperatively Interacting Vehicles Cham : Springer International Publishing AG,c2024 ISBN 9783031604935
Language:
English
Keywords:
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