Tempe Police Release Video of Uber Accident
I currently work full-time in the self-driving vehicle industry. I am part of a team that builds perception algorithms for autonomous navigation. I have been working exclusively with LiDAR systems for over 1.5 years.<p>Like a lot of folks here, my first question was: &quot;How did the LiDAR not spot this?&quot;. I have been extremely interested in this and kept observing images and videos from Uber to understand what could be the issue.<p>To reliably sense a moving object is a challenging task. To understand&#x2F;perceive that object (i.e., shape, size, classification, position estimate, etc.) is even more challenging. Take a look at this video (set the playback speed to 0.25): <a href="https:&#x2F;&#x2F;youtu.be&#x2F;WCkkhlxYNwE?t=191" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;WCkkhlxYNwE?t=191</a><p>Observe the pedestrian on the sidewalk to the left. And keep a close eye on the laptop screen (held by the passenger on right) at the bottom right. Observe these two locations by moving back and forth +&#x2F;- 3 seconds. You&#x27;ll notice that the height of the pedestrian varies quite a bit.<p>This variation in pedestrian height and bounding box happens at different locations within the same video. For example, at 3:45 mark, the height of human on right wearing brown hoodie, keeps varying. At 2:04 mark, the bounding box estimate for pedestrian on right side appears to be unreliable. At 1:39 mark, the estimate for the blue (Chrysler?) car turning right jumps quite a bit.<p>This makes me believe that their perception software isn&#x27;t as robust to handle the exact scenario in which the accident occurred in Tempe, AZ.<p>I think we&#x27;ll know more technical details in the upcoming days&#x2F;weeks. These are merely my <i>observations</i>.