Prediction Metrics (Metrics Episode 10)
You need to drive not where the free space is, but where the free space is going to be when you get there. That means perception classification errors can affect not only the "what" but also the "future where" of an object.
Prediction metrics deal with how well a self driving car is able to take the results of perception data and predict what happens next so that it can create a safe plan.
There are different levels of prediction sophistication required depending on operational conditions and desired own-vehicle capability. The first, simplest prediction capability is no prediction at all. If you have a low speed vehicle in an operational design domain in which everything is guaranteed to also be moving at low speeds and be relatively far away compared to the speeds, then a fast enough control loop might be able to handle things based simply on current object positions. The assumption there would be everything’s moving slowly, it’s far away, and you can stop your vehicle faster than things can get out of control. (Note that if you move slowly but other vehicles move quickly, that violates the assumptions for this case.)
The prediction basically amounts to, nothing moves fast compared to its distance. But even here, a prediction metric can be helpful because there’s an assumption that everything is moving slow compared to its distance away. That assumption might be violated by nearby objects moving slowly but a little bit too fast because they’re so close, or by far away things moving fast such as a high speed vehicle in an urban environment that is supposed to have a low speed limit. The frequency at which the assumption is violated that things move slowly compared to the distance away will be an important safety metric.
For self driving cars that operate at more than a slow crawl. You’ll start to need some sort of prediction based on likely object movement. You often hear: "drive to where the free space is" with the free space being the open road space that’s safe for a car to maneuver in.
But that doesn’t actually work once you’re doing more than about walking speed, because it isn’t where the free space is now that matters. What you need to do is to drive to where the free space is going to be when you get there. Doing that requires prediction because many of the things on the road move over time, changing where the free space is one second from now, versus five seconds from now, versus 10 seconds from now.
A starting point for prediction is assuming that everything maintains the same speed and direction as it currently has and update the speeds and directions periodically as you run your control loop. Doing this requires tracking so that you know not only where something is, but also what its direction and speed are. That means that with this type of prediction, metrics having to do with tracking accuracy become important, including distance, direction of travel and speed.
For safety it isn’t perfect position accuracy on an absolute coordinate frame that matters, but rather whether tracking is accurate enough to know if there’s a potential collision situation or other danger. It’s likely that better accuracy is required for things that are close and things that are moving quickly toward you and in general things that pose collision threats.
For more sophisticated self driving cars, you’ll need to predict something more sophisticated than just tracking data. That’s because other vehicles, people, animals and so on will change direction or even change their mind about where they’re going or what they’re doing.
From a physics point of view, one way to look at this is in terms of derivatives. The simplest prediction is the current position. A slightly more sophisticated prediction has to do with the first derivative: speed and direction. An even more sophisticated prediction would be to use the second derivative: acceleration and curvature. You can even use the third derivative: jerk or change in acceleration. To the degree you can predict these things, you’ll be able to have a better understanding of where the free space will be when you get there.
From an every day point of view, the way to look at it is that real things don’t stand still -- they move. But when they’re moving, they change direction, they change speed, and sometimes they completely change what they’re trying to do, maybe doubling back on themselves.
An example of a critical scenario is a pedestrian standing on a curb waiting for a crossing light. Human drivers use the person’s body language to tell the pedestrian is a risk of stepping off the curb even though they’re not supposed to be crossing. While that’s not perfect, most drivers will have stories of the time they didn’t hit someone because they noticed the person was distracted by looking at their cell phone or the person looked like they were about to jump into the road and so on. If you only look at speed and possibly acceleration, you won’t handle cases in which a human driver would say, “That looks dangerous. I’m going to slow down to give myself more reaction time in case behavior changes suddenly.”
It isn’t just the current trajectory that matters for a pedestrian. It’s what the pedestrian’s about to do, which might be a dramatic change from standing still to running across through to catch a bus.
The same would hold true for a human driver of another vehicle that you have some telltale available that suggests they’re about to swerve or turn in front of you. For even more sophisticated predictions, you probably don’t end up with a single prediction, but rather with a probability cloud of possible positions and directions of travel over time, where keeping on the same path might be the most probable. But a maximum command authority, right turn left turn, accelerate, decelerate might all be possible with lower probability but not zero probability. Given how complicated prediction can be, metrics might have to be more complicated than simply "did you guess exactly right?" There’s always going to be some margin of error in any prediction, but you need to predict in a way that results in acceptable safety even in the face of surprises.
One way to handle the prediction is to take a snapshot of the current position and the predicted movement. Wait a few control loop cycles, some fractions of a second or a second. Then check to see how it turned out. In other words, you can just wait a little while, see how well your prediction turned out and keep score as to how good your prediction is. In terms of metrics, you need some sort of bounds on the worst case error of prediction. Every time that bound is violated, it is potentially a safety-related event and should be counting it as a metric. Those bounds might be probabilistic in nature, but at some point there has to be a bound as to what is acceptable prediction error and what’s not.
To the degree that prediction is based on object type, for example, you’re likely to assume a pedestrian typically cannot go as fast as a bicycle, but that a pedestrian can jump backwards and pivot turn. You might want to know if the type-specific prediction behavior is violated. For example, a pedestrian suddenly going from stop to 20 miles per hour crossing right in front of your car, might be a competitive sprinter that’s decided to run across the road, but more likely signals that electric rental scooters have arrived in your town and you need to include them in your operational design domain.
Prediction metrics might be related to the metrics for correct object classification if the prediction is based on the class of the object.
Summing up, sophisticated prediction of behavior might be needed for highly permissive operation in complex dense environments. If you’re in a narrow city street with pedestrians close by and other things going on, you’re going to need really good prediction. Metrics for this topic should focus not only on motion measurement accuracy and position accuracy, but also on the ability to successfully predict what happens next, even if a particular object performs a sudden change in direction, speed, and so on. In the end, your metric should help you understand the likelihood that you’ll correctly interpret where the free space is going to be so that your path planner can plan a safe path.
For the podcast version of this posting, see: https://archive.org/details/metrics-11-prediction-metrics
Thanks to podcast producer Jackie Erickson.