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Springer Decision-making Strategies for Automated Driving - Springer Theses

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Description

This Springer Theses volume presents a comprehensive decision-making and planning architecture designed to enhance the navigation capabilities of automated vehicles operating in urban environments with non-detailed, open-source maps. The system dynamically obtains road corridors from map data and integrates a camera-based lane detection system to update and expand navigable space, directly addressing intrinsic uncertainty and low-fidelity mapping challenges. An efficient, human-like local planner operates within a probabilistic framework to determine safe motion trajectories whilst maintaining path curvature continuity, enabling autonomous vehicles to make intelligent navigation decisions in complex urban settings.

Key Features

Springer Theses publication covering advanced decision-making architectures for autonomous vehicle navigation in urban environments with limited map data.

Camera-based lane detection system that dynamically updates navigable space to overcome uncertainty and enhance real-time navigation capability.

Probabilistic local planning framework that calculates safe motion trajectories whilst preserving natural path curvature for smooth autonomous driving.

Open-source map integration approach that dynamically extracts road corridors to improve navigation precision in complex urban conditions.

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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