|Funding for:||UK Students, EU Students|
|Funding amount:||£16,062 Standard PhD at UKRI rates|
|Placed On:||27th July 2022|
|Closes:||31st January 2023|
Since the mid-2000s the number of remote sensors (RADAR, Imaging, LiDAR) on cars has been increasing, supporting ever-more sophisticated Advanced Driver Assistance Systems (ADAS) and a strong push towards Autonomous Vehicles (AVs). The resolution of these sensors in terms of number of pixels, frame rate and dynamic range (bit depth) has also been increasing. Hence the required data rate between the sensor and the Electronic Control Unit (ECU) that uses the data to provide the function on the car has increased dramatically, each individual sensor might produce in the order of 10 Gbit/s of continuous data. The network bandwidth required to connect the suite of sensors to the ECU(s) is becoming a problem in its own right, given that the interconnections must be robust in the harsh temperature, weather and vibration/physical shock environment of a car.
One potential solution to this problem is to reduce the required bandwidth using digital compression. During this project the student will develop a bespoke lightweight compression solution appropriate to the raw images coming from an image sensor, the encoder algorithm being suitable for embedding on the sensor device. Various aspects of the system then need to be investigated and proven if the system is to be adopted for safety-critical automotive applications, including the range of possible compression rates, the effect of the compression at various degrees on an Image Recognition system receiving the data, the effect of bit errors being introduced into the data as it is transmitted between the encoder and decoder and the resilience of the system to pathological images.
ELIGIBILITY AND DESIRED QUALITIES
Prospective applicants are expected to have a minimum 2.1 undergraduate (BEng, MEng, BSc, MSci) and/or postgraduate masters’ qualification (MSc) with 65% or above.
The successful student will:
Knowledge of algorithms related to image processing, video compression and forward error correction would be a distinct advantage.
Funding Source – ONSemi
Funding Duration – 3.5 Years
Stipend - Standard PhD at UKRI rates: £16,062
Funding Eligibility – UK and UK domicile EU students
Type / Role: