Like parent said, it's low-light in the visible spectrum, but I'd totally expect these vehicles to scatter tons of light in non-visible spectrums, making these conditions well-lit in those spectrums. Like a bat using echolocation.

I think the point GP was making is that an attentive human driver may have been able to see the pedestrian much earlier than when she becomes visible on the video.

this, the human visual system adapts to darkness. Consider that the victim who obviously is crossing the street as part of their lifestyle has likely done this many times before, and of all the vehicles that could have hit the victim, the one that did happened to be one of uber's self-driving vehicles with a clearly inattentive driver behind the wheel. A driver paying attention to the road would have at least hit the brakes well before impact.

Intermittent street lights reduce the adaptation though and add glare - the human visual range for scotopic ('dark adapted') and mesopic (twilight conditions) vision is about 4 orders of magnitude of luminance (cd/m²) that the retina can perceive simulataneously from 0 to saturation, without adaptation (dilating pupil). Dark to light adaptation is very rapid and happens in fractions of a second, light to dark adaptation happens over minutes.

The eye will adapt to a mean level of light in the larger FOV (not fovea only) - that is why instrument clusters on cars need to be low-level lit, to not disturb this adaptation. Exterior light sources like headlights and street lights further influence adaptation and veiling glare can lead to light sources overshadowing smaller luminance signals and pushing them out of the range that the eye is adapted for.

Don't forget compression artifacts. A dark object against a dark background in a highly compressed video is going to get compressed into just looking like the background.

I downloaded the video directly and it looks like it's 133 kbps for the video data at about 360p, which is just abysmally low. So it's not surprising that it's difficult to pick out detail in such a contrasty scene given the degree of compression and the resolution.

I don't think modern digital sensors actually under perform human vision, or it's at least not obvious. They have made huge improvements in the last decade.

Also, When a digital camera records an image, a gamma curve is applied to it before display, which makes up for a bias against the darker portions which the digital equipment does not inherently have.

Considering the streetlights, I cannot imagine any excuse. This video will sadly give them the benefit of public doubt but anyone familiar with lighting digital video will be unconvinced that the video feed was the culprit.

I take a photo with my iPhone in night conditions and the image I see on the screen comes out way darker than I saw with my eyes. That has to be corrected for, no?

Yes, but if you take a photo with a Sony A7s it will vastly outperform your eye.

No. The term we need to introduce here is "Dynamic Range". It is pointless to say "Sony A7s can vastly outperform the eye" where it could have been set for a low light exposure. Human eye has an amazing dynamic range - I don't know the exact number today, but last time I checked was like 3 years ago and the cameras at the time (D800) were not even close to human dynamic range.

That's because you're comparing a single exposure from a digital camera. You can have dynamic range far in excess of the human eye with HDR techniques, by combining difference exposures and/or by exposing different pixels on the same image differently.