Tracked shipping to Austria with premium packaging for just 3,99 € 

Ship to
Austria
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Robust Environmental Perception and Reliability Control for Intelligent Vehicles
Type
Physical Book
Author
Publisher
Language
English
Pages
301
Format
Hardcover
Dimensions
23.4 x 15.6 x 1.9 cm
Weight
0.62 kg.
ISBN13
9789819977895
Categories

Robust Environmental Perception and Reliability Control for Intelligent Vehicles

Jue Wang (Author) · Huihui Pan (Author) · Xinghu Yu (Author) · Springer · Hardcover

Robust Environmental Perception and Reliability Control for Intelligent Vehicles - Pan, Huihui ; Wang, Jue ; Yu, Xinghu

Cheaper New Book Imported to Austria
Delivery: 13 Aug - 20 Aug Shipping: 13 to 17 business days.
174,27 €
Faster New Book Imported to Austria
Delivery: 03 Aug - 05 Aug Shipping: 5 to 6 business days.
185,63 €
Import costs and 10% VAT included in the price ✅
174,27 €

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Robust Environmental Perception and Reliability Control for Intelligent Vehicles"

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Hardcover.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews