Self-driving vehicles could be a popular means of transportation in the coming years. Before that can happen, however, scientists will have to do a lot of work to ensure that these vehicles will be safe and can efficiently navigate in human-populated places without hurting anyone.
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As self-driving vehicles are ultimately designed to move around both static and moving obstacles, they should be able to detect objects quickly and avoid them. One way to achieve this could be to develop models that can predict the future behavior of objects or people on the street, in order to estimate where they will be located when the vehicle approaches them.
Predicting future behavior in urban environments, however, can be a difficult task. It is even more difficult to predict human behavior, such as unexpected actions of pedestrians.
In Arizona last year, a 49-year-old woman named Elaine Herzberg was killed by one of Uber’s self-driving cars. This and other accidents sparked lots of debates about the safety of self-driving vehicles and about whether to test these vehicles in populated environments.
About a week ago, new documents released by the U.S. National Transport Safety Board (NTSB) revealed that Uber’s autonomous vehicle involved in last year’s fatal crash did not identify Herzberg as a pedestrian until it was much too late. The same reports suggests that the autonomous vehicle involved in the crash was never trained to detect pedestrians anywhere outside of a crosswalk.
Herzberg was jaywalking at the time of the accident, so the software flaws revealed by the NTSB report would explain why Uber’s self-driving vehicle failed to spot her, which ultimately caused her death. The new analyses released by NTSB could put a halt to the company’s self-driving vehicle program, which had started testing again in December 2018 after being put on hold for several months.
These recent findings show the need for a more advanced AI to be developed, as well as more reliable software before self-driving vehicles can be tested on actual roads.
Interestingly, some days before NTSB released these documents, a paper by researchers at Uber’s Advanced Technologies Group, the University of Toronto and UC Berkeley was pre-published on arXiv, introducing a new technique to predict pedestrian behavior called discrete residual flow network (DRF-NET). According to the researchers, this neural network can make predictions about future pedestrian behavior while capturing the inherent uncertainty in forecasting long-range motion.
More details about this neural network over at TechXplore.
(Image Credit: Jain et. al/ TechXplore)