Swift

A welcome screen greets you by name and hands over after a few seconds. It exists because its absence was conspicuous during our own test drive — nothing about getting into the car acknowledged that a person had arrived.

Then suggestions, built from past behaviour: the commute it knows you take at this hour, the temperature at your destination on arrival, charge before and after, and a forecast of how the autopilot expects to behave along the way. Accept it, adjust it, or throw it away.

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This is the most direct answer to the research. The route is broken into stretches, each marked with whether the system can handle it. Roadworks get a roadworks symbol. City streets get a city-streets note.

Cruise-control speed, the observation setting and the autopilot switch all live in the same view, so the decision and the controls are never more than a glance apart.

The most-requested feature in every interview and the survey was some form of predictability. The feed is that: the upcoming manoeuvres, in order, each coloured by how confident the car is that it can manage it alone.

Confidence is derived from map data and other drivers' cloud data, so you see how closely you need to watch the road before the situation arrives rather than during it. The percentage we first tried didn't read at a glance — colour does.

At speed on a dual carriageway, the car detects an unknown object ahead. Without any input it checks whether the adjacent lane is clear, finds that it is, and changes lane. An emergency stop at speed avoided.

The part that matters for trust is what happens in the cabin at the same moment: the cockpit is told what the object was and why the car moved, as it moves.

The feed warns that a section is coming up the autopilot can't manage. Instead of only handing control back, the driver can offer to teach it: recording starts, they drive the section themselves, and the route data goes back into the fleet.

Driving it yourself stays available at any point. Finishing a teaching run earns reward points — the thesis suggests charging credit.

A cyclist is registered in the car's lane. The system checks that both options are genuinely possible, then offers them: follow at an adjusted speed, or overtake. Whichever you pick, the autopilot keeps driving.

A second case — a pedestrian crossing the autopilot is unsure about — works the same way. The design goal was explicit: never let the car sit in a decision loop that a human can resolve in one tap.

Our survey asked for this one almost verbatim: a written or video log, because reading things in real time means staring at the screen instead of the road. Notifications hold the history; tapping one jumps to the event.

In the Journal you get the incident in full — how many happened on the trip, how long it took, how far it went, and a replay of the footage. Then you answer: acted correctly, report a problem, or unsure.

That answer improves the system, is shared anonymously with the manufacturer and other drivers, and earns a reward. The car explains itself, the driver judges it, and both get better.