| Description |
1 online resource |
| Series |
Springer theses |
|
Springer theses.
|
| Contents |
Introduction -- Literature Overview -- Decision Making Architecture -- Global Planning and Mapping -- Motion Prediction and Manoeuvre Planning -- Optimal Trajectory Generation -- Integration and Demonstrations |
| Summary |
This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail |
| Notes |
"Doctoral Thesis accepted by Universidad Politécnica de Madrid, Spain." |
| Bibliography |
Includes bibliographical references |
| Subject |
Automated vehicles -- Decision making
|
|
Intelligent transportation systems.
|
|
Highway & traffic engineering.
|
|
Operational research.
|
|
Robotics.
|
|
Technology & Engineering -- Civil -- General.
|
|
Business & Economics -- Operations Research.
|
|
Technology & Engineering -- Robotics.
|
|
Intelligent transportation systems
|
| Genre/Form |
Electronic books
|
| Form |
Electronic book
|
| ISBN |
9783030459055 |
|
3030459055 |
|
