Original Article by Garry Staunton on aimsun, to read the full article, please click here

The UKAEA owned Culham Science Centre is located in Oxfordshire and has some 10km of private (gated) roads and hosts approaching 2,500 tenants. As the roads are not classed as ‘the public highway’ we have been able to host trials of Level 4 self-driving vehicles1 for in excess of 4 years with a high degree of safety throughout. During this time we have had to address what is sometimes called the self-driving vehicle paradox: arguably the greatest benefit from introducing such vehicles is improved road safety, but the single biggest challenged faced at the moment is proving that they can operate safely.

Our experience of hosting self-driving vehicle trials means that our site tenants have developed an implicit level of trust in the technology. However, we recognise that building such trust takes time to build, but it can then rise or fall as time goes on. Hence we are always looking at what evidence our site users, and the wider public are going to need if they are to spend their money on, or place their children in, a self-driving vehicle. Within this context we recognise that engineering-led approaches to building an evidence base tends to be ‘technology push’ and as such focus on collecting miles. Whilst such approaches are essential, the downside of such approaches is that driving real-vehicles on real-roads is time consuming, ties up expensive (and scarce) vehicles and cannot guarantee that the vehicles will experience all of the scenarios we can envisage them needing to be able to negotiate. This is where simulation comes in, with in-silico vehicles being able to navigate around tens of thousands of scenarios in minutes.

In the robotics and AI sphere simulation is a well-recognised development tool and the impact its application can have on the development cycle has been shown to significant.

For example in 2017 NASA launched the Space Robotics Challenge2 as a virtual competition to advance robotic software and autonomous capabilities for space exploration missions on the surface of extra-terrestrial objects, such as Mars or the moon. The participants were asked to programme a NASA Valkyrie (prototype) humanoid robot to complete specified tasks using the Gazebo open-source robotics simulator3. This development in simulation, followed by implementation in ‘the real’ highlight the advantage of the approach in that the competition allowed multiple teams to look at the problem without having to queue up and take turns to utilise a scarce resource. The individual who ‘won’ the challenge was invited to implement his simulation based approach on one of only four physical Valkyrie robots, and it was reported that it took 3 hours to achieve this4.

However, simulation is not a panacea. The NASA challenge focused on a number of very difficult but well-defined tasks, and if the robot had been tasked with doing anything else it would have failed. This illustrates the strength of simulation where we can envisage allowing self-driving vehicles to demonstrate the ability of their systems to respond to well-defined challenges, and as such can be argued to demonstrate their ability to undertake the minimum range of manoeuvres that a robot/AI needs to be able to safely undertake. This ‘library’ can (and will) grow with time.

This approach of using simulation to address defined challenges is well developed, and much utilised, in the testing of self-driving vehicles, with companies such as Waymo regularly reporting how many billions of miles they have driven in a simulator5. As well as miles driven, simulators allow developers to explore how their systems will react under the tens of thousands of scenarios that exist within the library. Such libraries are highly dynamic and grow daily in the range and complexity of situations where the response of self-driving vehicles can be explored. As will soon be explained in forthcoming OmniCAV Blog taxonomies are emerging to allow these to be more systematically described and their coverage extended and the OmniCAV project is a leading contributor to this process.

However, no matter how good simulators are, unexpected things happen and we need to react to the seemingly random behaviour of individuals etc. (such as the pedestrian in Figure 1). As well as individual behaviour changing, so can the physical location – for example Figure 3 shows traffic on a road in Renfrewshire6 following heavy rain, where successful navigation of the flood requires knowledge of both where the road is, the water depth and its impact of steering/traction etc.


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