£77.09

Springer Decision-making Strategies for Automated Driving

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Description

Improve the navigation capabilities of automated vehicles with this specialized research from Springer. This book introduces an effective decision-making and planning architecture designed for urban environments where non-detailed, open-source maps are used. By addressing the challenges of intrinsic uncertainty and low-fidelity map data, this work provides a technical foundation for safer vehicle autonomy. The system described utilizes a method for dynamically obtaining road corridors from existing map information. To enhance the navigable space, it integrates a camera-based lane detection system that updates information in real-time. This approach ensures that the vehicle can operate effectively even when map data is imperfect. At the core of this architecture is an efficient, human-like local planner. Operating within a probabilistic framework, the planner determines safe motion trajectories to ensure continuity in complex urban settings. This text serves as a vital resource for those studying autonomous navigation, robotics, and advanced decision-making systems in unpredictable environments.

Key Features

Enhances automated vehicle navigation by utilizing an effective decision-making and planning architecture for urban settings.

Addresses map uncertainty by dynamically obtaining road corridors from open-source and non-detailed map information.

Integrates a camera-based lane detection system to update and enhance the navigable space in real-time.

Provides a solution for low-fidelity map data through continuous updates to the vehicle's navigable environment.

Uses an efficient, human-like local planner to determine safe motion trajectories within a probabilistic framework.

Ensures movement continuity for autonomous systems operating in complex and unpredictable urban environments.

Product Specifications

Format
Paperback
Domain
Amazon UK
Release Date
26 April 2021
Listed Since
31 March 2021

Barcode

No barcode data available

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