Driving Simulator for Connected Autonomous Vehicles

According to the World Health Organisation, the number of traffic accident-related deaths remains unacceptably high, with over 1.3 million people dying each year globally. This number continues to rise due to growing population and increasing motor vehicle uptake. It has been reported that vast majority of trauma-related fatalities occur in the prehospital phase, while nearly a half of those are preventable. Therefore, timely and precise crash detection systems and appropriate response from emergency services are critical for reducing preventable deaths. Existing systems are imprecise, unreliable, and computationally heavy.

An informal ongoing collaboration with Volkswagen, established during the summer project, allowed to explore multiple computer vision methods and deep learning algorithms for accident detection applied to an existing traffic surveillance infrastructure. The project team has been authorised to access every traffic surveillance camera in the city of Carmel, Indiana. This allows testing deep learning algorithms on real-time data around-the-clock, during different times of the day and weather conditions, and on varying road layouts. As an outcome of the summer project, fundamental principles of computer vision and deep learning were explored and multitude of software tools, necessary for the upcoming PhD research, were utilised. The groundwork for my own algorithmic development for this ongoing collaboration was established through deployment and benchmarking of state-of-the-art algorithms.

With human error being the predominant cause of vast majority of road accidents, computer vision-based accident detection systems can reduce preventable deaths. Once the appropriate level of technological sophistication is reached, connected autonomous vehicles will eliminate the human factor. The major PhD research is focused on contributing towards developing an autonomous driving simulator, which is an effective tool for generating and validating advanced driver-assistance systems.

Ongoing developments with Volkswagen provide technical training and research data that will be used for the simulator. For instance, inference data from Carmel, saved in CSV format, will be combined with OpenStreetMap data and used for developing virtual and mixed reality scenarios and modelling road networks in OpenDRIVE format. Furthermore, computer vision algorithms will be modified and optimised for the vehicle camera. The simulator will then be connected to the powertrain dyno to allow hardware-in-the-loop control of a physical vehicle and its subsystems.

For Volkswagen, this research aims to develop a traffic accident detection feature that would reduce trauma-related fatalities that occur in the prehospital phase following a traffic accident. For IAAPS, it will contribute towards putting together a hardware-agnostic Advanced Driver Assistance System simulation software system with the ability to easily upgrade the hardware setup to support motion platforms and 360-degree projection screens. With simulation testing being an efficient way of validating autonomous technology, such expertise will be beneficial for the institute, empowering the organisation to become a trailblazer in the automotive industry, making its inevitable future closer.

Impact Summary

This proposal has been developed from the ground up to guarantee the highest level of impact. The two principal routes towards impact are via the graduates that we train and by the embedding of the research that is undertaken into commercial activity. The impact will have a significant commercial value through addressing skills requirements and providing technical solutions for the automotive industry - a key sector for the UK economy.

The graduates that emerge from our CDT (at least 84 people) will be transformative in two distinct ways. The first is a technical route and the second is cultural.

In a technical role, their deep subject matter expertise across all of the key topics needed as the industry transitions to a more sustainable future. This expertise is made much more accessible and applicable by their broad understanding of the engineering and commercial context in which they work. They will have all of the right competencies to ensure that they can achieve a very significant contribution to technologies and processes within the sector from the start of their careers, an impact that will grow over time. Importantly, this CDT is producing graduates in a highly skilled sector of the economy, leading to jobs that are £50,000 more productive per employee than average (i.e. more GVA). These graduates are in demand, as there are a lack of highly skilled engineers to undertake specialist automotive propulsion research and fill the estimated 5,000 job vacancies in the UK due to these skills shortages. Ultimately, the CDT will create a highly specialised and productive talent pipeline for the UK economy.

The route to impact through cultural change is perhaps of even more significance in the long term. Our cohort will be highly diverse, an outcome driven by our wide catchment in terms of academic background, giving them a 'diversity edge'. The cultural change that is enabled by this powerful cohort will have a profound impact, facilitating a move away from 'business as usual'.

The research outputs of the CDT will have impact in two important fields - the products produced and processes used within the indsutry. The academic team leading and operating this CDT have a long track record of generating impact through the application of their research outputs to industrially relevant problems. This understanding is embodied in the design of our CDT and has already begun in the definition of the training programmes and research themes that will meet the future needs of our industry and international partners. Exchange of people is the surest way to achieve lasting and deep exchange of expertise and ideas. The students will undertake placements at the collaborating companies and will lead to employment of the graduates in partner companies.

The CDT is an integral part of the IAAPS initiative. The IAAPS Business Case highlights the need to develop and train suitably skilled and qualified engineers in order to achieve, over the first five years of IAAPS' operations, an additional £70 million research and innovation expenditure, creating an additional turnover of £800 million for the automotive sector, £221 million in GVA and 1,900 new highly productive jobs.

The CDT is designed to deliver transformational impact for our industrial partners and the automotive sector in general. The impact is wider than this, since the products and services that our partners produce have a fundamental part to play in the way we organise our lives in a modern society. The impact on the developing world is even more profound. The rush to mobility across the developing world, the increasing spending power of a growing global middle class, the move to more urban living and the increasingly urgent threat of climate change combine to make the impact of the work we do directly relevant to more people than ever before. This CDT can help change the world by effecting the change that needs to happen in our industry.