Autocar experienced a 13- mile section of the Grand Drive route – running from Cranfield to a service station on the M1 and back – in an autonomous Leaf similar to the one that completed the record-breaking run. For the first three miles, the car followed a B-road that, during development, had been recreated in the virtual world by a team of engineers at Catapult, the organisation managing the programme.

Details such as broken lane markings, cambers and road signs were all replicated in the model. An autonomous Leaf then drove through the real environment, with its trajectory and behaviour overlaid onto this digital visualisation. Data from human drivers following the same route enabled engineers to compare the cars’ trajectories, with the aim of fine-tuning the Leaf’s autonomous responses to provide a more human-like experience.

During our ride, the Leaf behaved naturally, driving around a parked car smoothly, slowing for corners, holding its direction through changing cambers that might have thrown it off-line, and accelerating and slowing progressively through changing speed limits.

At the end of the road, we approached a complex roundabout above the M1 with multiple traffic lights. With the first set of lights showing red, the Leaf pulled up smoothly before, once they’d changed, pulling away to the next set, also on red. Soon, we were clear to drive down the exit ramp to the motorway and the Leaf gained speed progressively before joining the traffic. Once on the motorway, the car held position but, unlike on the Grand Drive, avoided overtaking.

David Moss, Nissan’s senior vice-president of R&D, said developing a system that feels natural will be key to winning customer support for autonomous technology. He said the focus on improving the accuracy of the sensors means that “our system is much less reliant on road markings. We combined this technology with the way our own drivers performed to give the system more human-like behaviour.

“We won’t be able to map every road in anything like the same detail that we mapped the test route’s first three miles, which is why the machine-learning work at MUEAVI that enables a computer to recall and learn from scenarios will be vital to the system’s development.”

Professor James Brighton, a senior lecturer at Cranfield University’s Advanced Vehicle Engineering Centre, added: “We wanted to make overtaking as comfortable as possible, so we improved forward planning to make the process smooth and secure-feeling.”

Brighton said that although some autonomous cars overtake in the most efficient manner, it doesn’t feel natural, so “the autonomous Leaf positions itself earlier, follows a more natural overtaking line and leaves a comfortable space between it and the other vehicle”.