£121.64

Springer - Robust Environmental Perception for Intelligent Vehicles

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Description

Explore the latest advancements in connected autonomous vehicle technologies with this comprehensive guide from Springer. This book provides a deep look into state-of-the-art algorithms designed for robust environmental perception and reliability control in intelligent vehicle systems. By combining object detection, semantic segmentation, and trajectory prediction, the text offers a systematic approach to managing complex road traffic scenes. It covers essential components such as multi-object tracking, multi-sensor fusion, and reliability control to ensure vehicles operate safely. A key focus of this work is the use of multi-sensor data fusion-based neural networks. These methods address environmental perception fault tolerance, helping to solve reliability problems that occur when certain sensors face challenges. This resource is an essential addition for those studying the intersection of AI, machine learning, and autonomous transportation safety.

Key Features

Covers state-of-the-art algorithms for robust environmental perception and reliability control in intelligent vehicle systems.

Integrates object detection, semantic segmentation, and trajectory prediction for comprehensive traffic scene management.

Provides systematic methods for multi-object tracking and multi-sensor fusion to improve vehicle safety.

Addresses perception reliability issues using multi-sensor data fusion-based neural networks.

Focuses on fault tolerance algorithms to maintain performance during sensor challenges in complex environments.

Product Specifications

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

Barcode

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