It certainly looks bad for Uber
Major Update: Release of the NTSB full report includes several damning new findings
The Tempe police released the poor quality video from the Uber. What looks like a dash-cam video along with a video of the safety driver. Both videos show things that suggest serious problems from Uber, absent further explanation.
You can watch the video here if you have not seen it. It's disturbing, though the actual impact is removed. It will make you angry. It made me angry.
Above I have included a brightened frame from 3 seconds into the video. It is the first frame in which the white running shoes of the victim are visible in the dashcam video. They only appear then because she is previously in darkness, crossing at a poorly lit spot, and the headlamps finally illuminate her. Impact occurs at about 4.4 seconds (if the time on the video is right.)
She is crossing, we now see, at exactly this spot where two storm drains are found in the curb. It is opposite the paved path in the median which is marked by the signs telling pedestrians not to cross at this location. She is walking at a moderate pace.
The road is empty of other cars. Here are the big issues:
- On this empty road, the LIDAR is very capable of detecting her. If it was operating, there is no way that it did not detect her 3 to 4 seconds before the impact, if not earlier. She would have come into range just over 5 seconds before impact.
- On the dash-cam style video, we only see her 1.5 seconds before impact. However, the human eye and quality cameras have a much better dynamic range than this video, and should have also been able to see her even before 5 seconds. From just the dash-cam video, no human could brake in time with just 1.5 seconds warning. The best humans react in just under a second, many take 1.5 to 2.5 seconds.
- The human safety driver did not see her because she was not looking at the road. She seems to spend most of the time before the accident looking down to her right, in a style that suggests looking at a phone.
- While a basic radar which filters out objects which are not moving towards the car would not necessarily see her, a more advanced radar also should have detected her and her bicycle (though triggered no braking) as soon as she entered the lane to the left, probably 4 seconds before impact at least. Braking could trigger 2 seconds before, in theory enough time.)
To be clear, while the car had the right-of-way and the victim was clearly unwise to cross there, especially without checking regularly in the direction of traffic, this is a situation where any properly operating robocar following "good practices," let alone "best practices," should have avoided the accident regardless of pedestrian error. That would not be true if the pedestrian were crossing the other way, moving immediately into the right lane from the right sidewalk. In that case no technique could have avoided the event.
This is not a complex situation. This is the sort of thing that the LIDAR sees, and it sees very well at night. The Uber car, at 40mph, is getting into the upper range of speeds at which it is safe to drive non-freeway with just the LIDAR. What I called the valley of danger four years ago, and Uber knows it. 40mph is about as fast as you should go, but you can do it. (Even so, some cars like to go a bit slower approaching legal crosswalks, marked or not.) Using the LIDAR their perception system should have had a pretty good impression of her by 50m (2.7 seconds) and applied the brakes hard. The stopping distance is 25m or less with hard braking. (A more typical strategy would be to slow, and get a better appraisal, and then continue braking to as to stop a 2-3m before her, to avoid jarring any passengers.)
Uber needs to say why this did not happen. I have seen one report -- just a rumour from somebody who spoke to an un-named insider, that the LIDAR was off in order to test operations using just camera and radar. While that might explain partly what happened, it is hard to excuse. Even if you want to do such tests -- many teams are trying to build vehicles with no LIDAR -- the LIDAR should remain on as a backup, triggering braking in exactly this sort of situation when the other systems have failed for some reason, or at least triggering a warning to the safety driver. It would be highly unwise to just turn it off.
In fact, I have to say that this sort of impact would have been handled by the fairly primitive ADAS "Forward Collision Warning" systems found on a large number of cars. Not the most basic radar-only ones that don't detect horizontally moving objects, but any of the slightly more sophisticated ones on the market. The unit standard in the Volvo XC90 promises it will reduce velocity by 50km/h if a bicycle crosses your path. The built in systems that come with these cars are typically disabled in robocar operation.
You may wonder, if there is LIDAR data, why have the police not examined it? While it is possible they have, they may not be equipped to. Police can readily examine videos. Understanding point clouds is difficult without Uber's own suite of tools, which they may not have yet offered to police, though they will need to if legal proceedings take place. Remember that because the victim crossed directly at a "don't cross" location, the car had the right of way, which is all the police are usually concerned with. Police may be concerned over Arizona law requirements to brake even for jaywalkers. However, the police may only consider a human's abilities here, not the superhuman vision of LIDAR or radar. From the dashcam video, it seems that there was very little time to react, and no fault for a human driver hitting somebody who "came out of nowhere." The police may not have a good way to evaluate the vastly superior dynamic range of human vision compared to the camera.
Waymo's cars, and a few others use long-range LIDARs able to see 200m or more. Such a LIDAR would have detected the victim as soon as she became visible on the median, though most systems do not react to pedestrians off the road. As soon as she set foot on the road, that would be a flag to such a system. One lesson from this accident might well be to map "illegal, but likely crossings" and exercise certain caution around them. In many countries, people even cross freeways routinely, though this is very dangerous because nobody can react in time at such speeds.
There is a dark irony that this longer range LIDAR is what the Waymo vs. Uber lawsuit was about. Though I doubt that Uber would have had its own long range LIDAR in production by now if they had not been troubled by that lawsuit.
It should be noted because the victim is wearing a black shirt, some of the numbers on the LIDAR range may be reduced, but not a great deal. Would need to know the reflectivity of the cloth. If it was less than 10% (very black) it's an issue, though she was not fully covered. She has blue jeans, bright hair and a red bike.
It should also be noted that while driving without a LIDAR's help if you have one is unwise, many teams, most famously Tesla, are developing cars with no LIDAR at all. It not a problem to drive a prototype which is not ready for deployment yet, that is what everybody is doing, and it's why they have safety drivers and other safety backups. However, those drivers must be diligent and the cars operated only where they are qualified to operate.
Cameras and HDR
The Uber reportedly has a large array of cameras. Good. That usually means that the system has been designed to do "high dynamic range" vision for driving at night. That's because what we see here is common -- uneven lighting due to the headlamps and streetlamps. This means either 2 or more cameras with different exposure levels, or one camera constantly switching exposure level to capture both lit and unlit objects.
A vision system based on HDR should also have easily seen her and triggered the stop.
Another option, not used on most cars today, is a thermal "night vision" camera. I have written about these a few times and I experimented with them while at Google back in 2011. Then (and even now) they are quite expensive, and must be mounted outside the glass and kept clean, so teams have not been eager to use them. Such a camera would have seen this pedestrian trivially, even if all the lights were off (headlights, streetlamps etc.) (LIDAR also works in complete darkness.) I have not heard of Uber using such night-vision cameras.
Note that the streetlamps are actually not that far from her crossing point, so I think she should have been reasonably illuminated even for non-HDR cameras or the human eye, but I would need to go to the site to make a full determination of that.
Once you have a properly exposed image from a camera, several vision techniques are used to spot obstacles within it. The simplest one is the use of "stereo" which requires 2 cameras, anywhere from 8 inches to 4 feet part in most cars. That can identify the distance to objects, though it is much better when they are close. It would not detect a pedestrian 200 feet away but can, if wide and high-resolution see 150 feet.
The second method is detecting motion. There is the motion of close objects against the background if they are not directly in front of you. There is also the motion against the background when the objects are moving, as a pedestrian crossing the road is.
Finally, the area of most research is the use of computer vision, usually powered by new machine learning techniques, to recognize objects from their appearance, as humans do. That's not perfect yet but it's getting pretty good. It can, in theory see quite far away if the camera is high resolution at the distance in question.
Radar could have helped here, but the most basic forms of radar would not help because a pedestrian slowly crossing the street returns a Doppler signature similar to a stationary object -- ie. just like all the signs, poles, trees and other fixed objects. Because radar resolution is low, many radars just ignore all stationary (meaning not moving towards or away from the car) objects. More advanced radars with better resolution would see her, but their resolution is typically only enough to know what lane the stationary target is in. Radar-based cars generally don't respond to a stationary object in the next lane, because as a driver you also don't slow because a car is stopped in a different lane, when your lane is clear. Once she entered the Uber's lane, the radar should have reported a potential stationary object in the lane which should have been a signal to brake. It's not quite as easy as I lay out here, unfortunately. Even these good radars have limited vertical resolution and so are often not enough on their own.
My guess is she is only squarely in the lane about 1.5 seconds before impact, which with decision making time may not be enough. You need to start super hard braking 1.4 seconds before impact at 40mph)
The safety driver
Clearly there is a problem with the safety driver. She is not doing her job. She may face legal problems. She will certainly be fired. The real debate will be over Uber's policies on hiring, training and monitoring safety drivers, and the entire industry's policies.
Uber was operating this car with only one safety driver. Almost all other teams run with two. Typically the right-seat one is monitoring the software while the left-seat one monitors the road. However, the right-seat "software operator" (to use Google's term) is also a second pair of eyes on the road fairly frequently.
Human beings aren't perfect. They will glance away from the road, though it is not possible to justify the length of time this safety driver was not looking. We will be asking questions about how to manage safety drivers. It is possible to install "gaze tracking" systems which can beep if a driver looks away from the road for too long a time. We all do it, though, and get away without accidents almost all the time.
We may see applicants for this job tested for reaction times and ability to remain attentive through a long grinding day, or see them given more breaks during the day. If phone use was an issue, it may be necessary to lock up phones during operations.
It is very likely that the safety driver's mistakes will pass on to Uber through the legal principles of vicarious liability. There is even criminal vicarious liability in extreme cases.
Passengers who have ridden in Uber's vehicles get to look at a display where the software shows its perception output, ie. a view of the world where it identifies its environment and things in it. They report that the display has generally operated as expected detecting pedestrians, including along the very road in question, where they have seen the vehicle slow or brake for pedestrians entering the road outside crosswalks. Something about that failed.
It is also worth considering that the police report suggested that no braking took place at all. Even detecting and hard braking at one second might have reduced the impact speed enough to make it non-fatal. I have not done the math, but braking and swerving, even from 1 second out, might have been able to avoid the impact on the woman. (As I explain in earlier posts, most cars are reluctant to swerve, because you can't depend on doing that and it can make the situation worse.)
The Arizona code states that at all times, drivers must, "Exercise due care to avoid colliding with any pedestrian on any roadway."
I've seen some call to relegate prototype robocars to test tracks and simulation. This is where they all start, but to reach safety, you can only do .1% of your testing there. There is truly no alternative yet known to develop and prove these cars than operation on real roads with other road users, exposing them to some risk. The discussion is how much risk they can be exposed to and how it can be mitigated.
It's also important to realize that these cars are prototypes, and they are expected to fail in a variety of ways, and that is why they have safety drivers performing oversight.
This accident will make us ask just how much risk is allowed, and also to examine how well the safety driver system works and how it can be improved. We are shocked that Uber was operating a car that did not detect a pedestrian in the middle of the road, and shocked that the safety driver failed at her job to take over if that happens. But we must understand that the prototype vehicles are expected to fail in different ways. I don't think a vehicle should have failed in so simple a way as this, but most of these cars in testing still get software faults on a fairly frequent basis, and the safety drivers take over safely. The answer is not to demand perfection from the cars or we can never prototype them. Sadly, we also can't demand perfection from human safety drivers either. But we can demand better than this.
This will set Uber's efforts back considerably, and that may very well be the best thing, if it is the case that Uber has been reckless. It will also reduce public trust in other teams, even though they might be properly diligent. It may even sink Uber's efforts completely, but as I have written, Uber is the one company that can afford to fail at developing a car. Even if they give up now, they can still buy other people's cars, and maintain their brand as a provider of rides, which is the only brand they have now.
I suspect it may be a long time -- perhaps years -- before Uber can restart giving rides to the public in their self-driving cars. It may also slow down the plans of Waymo, Cruise and others to do that this year and next.
At this point, it does seem as though a wrongful death lawsuit might emerge from the family of the victim. The fame for the lawyer will cause pro bono representation to appear, and the deep pockets of Uber will certainly be attractive. I recommend Uber immediately offer a settlement the courts would consider generous.
And tell us more information about what really happened. And, if it's as surmised, to get their act together. The hard truth is, that if Uber's vehicle is unable to detect a pedestrian like this in time to stop, Uber has no business testing at 40mph on a road like this. Certainly not with an inattentive solo safety driver.