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Springer Robust Environmental Perception and Reliability Control - Intelligent Vehicles

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Description

This book explores state-of-the-art algorithms for robust environmental perception and reliability control in intelligent vehicle systems. It systematically integrates object detection, semantic segmentation, trajectory prediction, multi-object tracking, and multi-sensor fusion to ensure safe operation in complex road traffic scenes. The work addresses a critical challenge in autonomous vehicle development: maintaining perception reliability when sensor data is incomplete or degraded. By combining multi-sensor data fusion with neural networks and fault tolerance algorithms, this volume provides researchers, engineers and developers with comprehensive solutions for building more dependable autonomous systems. Part of the Recent Advancements in Connected Autonomous Vehicle Technologies series, it bridges the gap between theoretical perception algorithms and practical reliability requirements for real-world vehicle deployment.

Key Features

Covers object detection, semantic segmentation, trajectory prediction and multi-object tracking for autonomous vehicle perception systems.

Integrates multi-sensor data fusion techniques with neural networks to improve environmental perception accuracy.

Addresses perception fault tolerance and reliability control when sensor data is missing or compromised.

Part of the Recent Advancements in Connected Autonomous Vehicle Technologies series, volume 4.

Published by Springer for researchers and engineers developing intelligent vehicle systems.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
27 November 2024
Listed Since
28 November 2024

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