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商品コード: 9781138030190

Predicting Vehicle Trajectory

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書名

Predicting Vehicle Trajectory
著者・編者 Barrios, C. & Motai, Y.
出版社/発行元 CRC Press
発行年/月 2017年3月   
装丁 Hardcover
ページ数/巻数 190 ページ
ISBN 978-1-138-03019-0
発送予定 海外倉庫よりお取り寄せ 2-3週間以内に発送します

 

Desciption

 

This book concentrates on improving the prediction of a vehicle’s future trajectory, particularly on non-straight paths. Having an accurate prediction of where a vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. The US DOT will be mandating that all vehicle manufacturers begin implementing V2V and V2I systems, so very soon collision avoidance systems will no longer rely on line of sight sensors, but instead will be able to take into account another vehicle’s spatial movements to determine if the future trajectories of the vehicles will intersect at the same time. Furthermore, the book introduces the reader to some improvements when predicting the future trajectory of a vehicle and presents a novel temporary solution on how to speed up the implementation of such V2V collision avoidance systems. Additionally, it evaluates whether smartphones can be used for trajectory predictions, in an attempt to populate a V2V collision avoidance system faster than a vehicle manufacturer can.

Features

- MATLAB Code for proposed methods.
- Real Sensory Datasets (GPS, Accelerometer, ODBII, Scan-Tool) for download.
- This book introduces the reader to some improvements when predicting the future trajectory of a vehicle, and it also presents a novel temporary solution on how to speed up the implementation of such V2V collision avoidance systems when only a small number of vehicles on the road will have this from the manufacturer.
- Discusses smartphone implementation

 

Contents:

 

CHAPTER 1: Improving Estimation of Vehicle’s Trajectory Using Latest Global Positioning System with
Kalman Filtering
1.1. Introduction
1.2. Kalman Filter
1.3. Interacting Multiple Models Estimation
1.4. Geographical Information System
1.5. Experimental Results
1.6. Conclusions
1.7. References

CHAPTER 2: Asynchronous Heterogeneous Sensor Fusion using Dead Reckoning and Kalman Filters
2.1. Introduction
2.2. Position Estimation Techniques
2.3. Dead Reckoning with Dynamic Error (DRWDE) using Kalman Filters
2.4. Evaluation Criteria
2.5. Experimental Performance of the DRWDE System
2.6. Conclusions
2.7. References

CHAPTER 3: Can Smartphones Fill in the V2V/V2I Implementation Gap?
3.1. Introduction
3.2. Position Estimation with Kalman Filters
3.3. Position Estimation Framework Using GPS and Accelerometer Sensors
3.4. Car and Smartphone Sensors Setup for a V2V/V2I System
3.5. Evaluation Criteria
3.6. Experimental Evaluation
3.7. Conclusions
3.8. References

CHAPTER 4: Conclusions

Appendix:

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