Question

If we were to use a simulation to understand how some software would perform in collisions. Given the following scenario, what variables would you manipulate to try and make a collision occur? Say for example, the self-driving car is approaching an intersection with a yellow light for their direction of travel, there is also a car on the road following closely behind it in the same direction, in the same lane. The two cars come to a stop without incident in this example. What variables would you manipulate in simulation to cause a collision?

Please list all the variables as well.

Answer 1

With simulation, we can turn a single real-world encounter — such as a flashing yellow left turn — into thousands of opportunities to practice and master a skill. Here’s how it works:
Step 1: Start with a highly-detailed vision of the world.
Our first step is to make our virtual world super-realistic. Using a powerful suite of custom-built sensors, we can build a virtual replica of this complicated left turn in Mesa, complete with identical dimensions, lanes, curbs, and traffic lights. One of the key advantages of simulation is that you can focus on the most interesting interactions — flashing yellow signals, wrong-way drivers, or nimble pedestrians and cyclists — rather than monotonous highway miles.
Step 2: Drive, drive, and redrive.
With this flashing yellow left turn now digitized in our virtual world, our software can practice this scenario thousands of times over. Every time we tweak the software, we can instantaneously test the change at the same intersection in identical driving conditions. That’s how we were able to teach our cars to naturally inch forward at that flashing yellow light, and slot in after oncoming traffic. What’s more, in simulation we can practice this new skill on every flashing yellow arrow we’ve ever come across (at last count there’s more than 60 of these intersections in Chandler, AZ alone). This means our software can get smarter, faster.
Step 3: Create thousands of variations.
Next, we can take this one tricky left turn and multiply it to explore thousands of variable scenarios and “what ifs?” Through a process called fuzzing, we can alter the speed of oncoming cars and the timing of traffic lights to make sure our vehicles can still find the right gap in traffic to make a safe and smooth turn. We can also make the street scene busier and more complex by adding vehicles, pedestrians and cyclists that never existed in the original scene. With fuzzing, we can even simulate motorcycles “splitting the lane,” or joggers zig-zagging across the street to see how that might change our driving on the road.
Step 4: Verify, validate, iterate.
Success: our self-driving car has learned how to confidently turn at a flashing yellow arrow. That new skill then becomes part of our car’s permanent knowledge base, and will be shared with every vehicle across the fleet. In turn, we’ll use real-world driving and our private test track to verify and validate our experience in simulation. And then the cycle begins again.