Automotive system for remote surface classification
- Submitting institution
-
The University of Birmingham
- Unit of assessment
- 12 - Engineering
- Output identifier
- 43279885
- Type
- D - Journal article
- DOI
-
10.3390/s17040745
- Title of journal
- Sensors
- Article number
- 745
- First page
- -
- Volume
- 17
- Issue
- 4
- ISSN
- 1424-8220
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a unique research outcome combining polarimetric microwave radar, ultrasonic sensing, and AI based deeply modified neural network algorithms for signal processing, to enable remote classification of the road surface type ahead of a vehicle to enable optimum automated progress control. The innovative technology has high scientific and industrial impact and was internationally patented (JLR-WO-2015/121107-A1) by the authors and JLR. The initial fundamental research led to the development a complete compact prototype evaluation and demonstration system on a JLR vehicle to extend world class all-terrain capability (https://media.jaguarlandrover.com/news/2016/07/jaguar-land-rover-
demonstrates-all-terrain-self-driving-technology). This triggered a multimillion Innovate UK project CORESENCE.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
