Welcome back to Transportation Weekly; I’m your web host Kirsten Korosec, mature transport reporter at TechCrunch. This is the second edition and significantly people, this week what happened? Too much. Much Too! Heard about TechCrunch’s Transportation Weekly Never? Catch up here. As I’ve written before, think about this a soft start. Follow me on Twitter @kirstenkorosec to make sure you see it every week.
Off we go … vroom. A couple of OEMs in the motor vehicle world. And here, (wait for it) there are ONMs – original news manufacturers. That’s where investigative reporting, business analysis and items on transportation lives. This week, we’ve got some insider info on Didi, China’s largest ride-hailing firm. China-based TechCrunch reporter Rita Liao discovered from resources that Didi plans to lay off 15 percent of its employees, or about 2,000 people this year. CEO Cheng Wei made the announcement during an interior meeting Friday morning. Find out about it here.
Didi’s troubles with regulators and its backlash from two high-profile traveler murders last year don’t exist in a vacuum. Their problems are in line with what is occurring in the ride-hailing industry, particularly in more mature markets where the novelty has worn off and towns have woken up. For companies like Didi, Uber, Lyft and other emerging players, this means more resources (capital and people) spent dealing with cities as well as researching to diversify their businesses.
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All the while, they must still connect away at the nagging problems of reducing costs and keeping riders and drivers. Look at Uber Just. As Megan Rose Dickey reports, Uber’s stiff losses continued in the fourth quarter. The upshot: Its loss can be related to increased competition and significant investment in bigger wagers like micro mobility and Elevate. And legal fees apparently. Uber, The Verge reports, on Friday to overturn a rules that hats motorists sued NYC. This week, TechCrunch editor Devin Coldewey digs into the development of something that can estimate not merely where a pedestrian is headed, but their too present and gait.
The University of Michigan, well known for its initiatives in self-driving car technology, has been working on an improved algorithm for predicting the movements of pedestrians. These algorithms is often as simple as determining a individual and seeing just how many pixels move more than a few frames, extrapolating from there then.
But naturally, human being movement is a little more technical than that. Few companies advertise the exact degree of fine detail with which they solve human styles and movement. This known degree of granularity seems beyond what we’ve seen. UM’s new system uses LiDar and stereo camera systems to estimate not only the trajectory of a person, but their pose and gait. Pose can indicate whether one is looking towards or from the automobile away, or using a cane, or stooped over the phone; gait signifies speed and purpose.
Is someone glancing over their shoulder? Maybe they’re around heading to show, or head into traffic. This additional data helps something predict movement and makes for a more complete group of navigation programs and contingencies. Importantly, it performs well with only a small number of frames to utilize – perhaps comprising an individual step and swing of the arm. That’s enough to make a prediction that is better than simpler models handily, a critical measure of performance as one cannot assume that a pedestrian will be noticeable for any lots of structures between obstructions.
Not too much can be done with this noisy, little-studied data right now, but perceiving and cataloguing it’s the first step to making it an integral part of an AV’s vision system. We hear a complete great deal. But we’re not selfish. Every big funding round comes with an origin tale – that magic instant when planets align and a capitally-flush investor gazes across an area at just the right time and places the perfect company looking for funds and guidance.