Three Approaches to Solving the Autonomous Vehicle Orientation Problem

What different driverless car technologies reveal about their overall strategies

Out of all the elements above, orientation (or knowledge of one’s position relative to the surroundings) seems to be the most difficult for driverless cars to master, and that has to do with the dynamic nature of cities. Construction sites, road closures, new signs, and missing road markings are just a few examples of the kind of uncertainty that can change urban surroundings to the point of confusing even humans, let alone software. Naturally, there is no single method of dealing with the problem. Recent advances, however, reveal three noteworthy strategies.

Tesla’s approach to orientation has been to pack as much intelligence into each individual car as possible. Rather than rely on pre-recorded maps, Elon Musk wants to combine image processing and machine learning to give each Tesla vehicle a real-time knowledge of its surroundings. Tesla vehicles learn as they go and share their knowledge with other cars. They rely on the world around them rather than on historical data (maps, etc.), they don’t run the risk of relying on an outdated map. However, this kind of real-time processing adds a lot of complexity to the car.

Another approach to autonomy in driving is (counter-intuitively) not focusing so much on making cars smarter to adapt to their environment, but rather creating smarter environments. This eases the burden on vehicles to be able to figure out all the uncertain elements in their environment. In this scenario, the environment would alert the vehicle of changing surroundings and let them know with a higher precision what the surrounding conditions are. Think construction cones that can tell an incoming car where exactly the construction area is and where the temporary lanes are.

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