Complexity Science Doctoral Training Centre Mini Project
Research Towards Advanced Driver Assistant Systems
Supervisor: Dr Yingping Huang, Senior Research Fellow, Warwick Manufacturing Group
(WMG) Email: yingping.huang@warwick.ac.uk, Telephone: 02476575928
Co-supervisor: Alain Dunoyer, Technical Specialist, Jaguar & Land Rover (JLR) Research
In order to improve traffic safety, both the scientific community and the automobile industry
have been contributing to the research and development of Advanced Driver Assistant
Systems (ADAS). These systems are to enable a car to be aware of its environment and warn
the driver of potentially hazardous situations or control the car to avoid traffic accidents.
Examples of ADAS are Adaptive Cruise Control, for automatically maintaining a safe time
gap with a preceding vehicle; Lane Departure Warning, for warning the driver in case the
vehicle starts to leave the lane inadvertently. These systems are applied to structured highway
scenarios. ADAS for urban traffic is still far away from mature, and creates grand challenges
for the research community. This is because urban traffic has much more complex
background information where the road scene is cluttered and diverse obstacles exists such as
pedestrians, vehicles, cyclists, traffic infrastructure.
The project will be a joint project between WMG and Jaguar Land Rover. JLR has recently
launched the new Surround Camera System that uses five cameras situated around the vehicle
to provide an almost complete 360 degree view of the environment to the driver. The vehicle
is also equipped with a laser scanner and a radar sensor for sensor fusion. The project will
investigate the sensing techniques including vision, laser scanner and radar, and aims to
deliver intelligent sensing solutions for vehicle environment recognition. The project will
majorly involve with the following subjects: image/signal processing, computer vision,
pattern recognition, machine learning and mathematics. The student will get a very good
exposure to real automotive problems and have the possibility to test his/her findings on the
JLR vehicle. Spending some time at JLR should also be possible.
In terms of functions of ADAS, the research project can be one or some of the following
topics;
• Threat Detection
> Pedestrian detection / classification
> Object detection
• External Environment Detection
> Speed over ground
> Terrain recognition
> Wading Aid - water level monitoring
> River flow measurement
> Fog, rain, light levels detection
• Road infrastructure Detection
> Lane tracking
> Road edge tracking
> Road sign recognition
References:
1) Huang, Y. and Ken, Y., “Binocular Image Sequence Analysis: Integration of Stereo Disparity and
Optic Flow for Improved Obstacle Detection and Tracking”, EURASIP Journal on Advances in
Signal Processing, vol. 2008, Article ID 843232, 10 pages, 2008. doi: 10.1155/2008/843232.
2) Huang, Y., Fu, S. and Thompson, C., “Stereovision-based Object Segmentation for Automotive
Applications”, EURASIP Journal on Applied Signal Processing, vol. 2005, no. 14, pp. 23222329, 2005. doi:10.1155/ASP.2005.2322.
3) Huang, Y., “Obstacle Detection in Urban Traffic Using Stereovision”, Proceedings of IEEE 8th
International Conference on Intelligent Transportation Systems, Vienna, pp. 633-638, 13th – 16th
Sept. 2005.