Robust Environmental Perception and Reliability Control for Intelligent Vehicles
Series: Recent Advancements in Connected Autonomous Vehicle Technologies; 4;
- Publisher's listprice EUR 181.89
-
75 438 Ft (71 846 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 15 088 Ft off)
- Discounted price 60 351 Ft (57 477 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
75 438 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1st ed. 2024
- Publisher Springer Nature Singapore
- Date of Publication 26 November 2023
- Number of Volumes 1 pieces, Book
- ISBN 9789819977895
- Binding Hardback
- No. of pages301 pages
- Size 235x155 mm
- Weight 641 g
- Language English
- Illustrations XI, 301 p. 140 illus., 137 illus. in color. Illustrations, black & white 507
Categories
Long description:
This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes.
- Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy.
- Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods.
- Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers.
- Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios.
- Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.
Table of Contents:
Chapter 1. Background.- Chapter 2. Robust Environmental Perception of Multi-Sensor Data Fusion.- Chapter 3. Robust Environmental Perception of Monocular 3D Object Detection.- Chapter 4. Robust Environmental Perception of Semantic Segmentation.- Chapter 5. Robust Environmental Perception of Trajectory Prediction.- Chapter 6 Robust Environmental Perception of Multi-object Tracking.- Chapter 7. Reliability Control of Intelligent Vehicles.- References.
More
Technikemotionen
41 060 HUF
Asset-Based Lending and Insolvency
47 751 HUF
42 976 HUF
