Amman, Jordan - About 20 years ago, a few girls were playing football in the streets of their neighbourhoods in Jordan. Most of them played with the boys, as football was not considered a girls' thing by the conservative society in the kingdom. There was no club, no coach - in fact, no kind of infrastructure existed for female football players. "It was very difficult in the beginning," remembers Stephanie al-Naber, who used to play in the streets when she was little. The society widely rejected the idea of girls playing football, an obstacle for many, but not all.
Except in fanciful movies like 2003's The Matrix Revolutions, where fearsome squid-like robots maneuvered with incredible ease, most robots are too clumsy to move around obstacles at high speeds. This is true in large part because they have trouble judging in the images they "see" just how far ahead obstacles are. This week, however, Stanford computer scientists will unveil a machine vision algorithm that gives robots the ability to approximate distances from single still images. "Many people have said that depth estimation from a single monocular image is impossible," says computer science Assistant Professor Andrew Ng, who will present a paper on his research at the Neural Information Processing Systems Conference in Vancouver Dec. 5-8. "I think this work shows that in practical problems, monocular depth estimation not only works well, but can also be very useful."
In this paper, we present a rigorous modular statistical approach for arguing safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of sufficient and necessary conditions at component level for the overall safety of the vehicle. A simple concrete example studied illustrates how perception system analysis at component level can be used to prove or disprove safety at the vehicle level.
For Muslims in the United States, there is no other time more centered around gathering in congregation than the holy month of Ramadan. In every corner of the country, believers attend community iftar meals to break the fast and then pack neatly into tight rows for nightly prayers at the mosque. On weekends, especially, some may linger longer as they catch up, share in the pre-dawn suhoor meal and line up again for the fajr, dawn, prayers.
Frankel, Richard Oliver (Stanford University) | Gudmundsson, Olafur (Stanford University) | Miller, Brett (Stanford University) | Potter, Jordan (Stanford University) | Sullivan, Todd (Stanford University) | Syed, Salik (Stanford University) | Hoang, Doreen (Stanford University) | John, Jae min (Stanford University) | Liao, Ki-Shui (Stanford University) | Nahass, Pasha (Stanford University) | Schwab, Amanda (Stanford University) | Yuan, Jessica (Stanford University) | Stavens, David (Stanford University) | Plagemann, Christian (Stanford University) | Nass, Clifford (Stanford University) | Thrun, Sebastian (Stanford University)
Lane changing on highways is stressful. In this paper, we present RASCL, the Robotic Assistance System for Changing Lanes. RASCL combines state-of-the-art sensing and localization techniques with an accurate map describing road structure to detect and track other cars, determine whether or not a lane change to either side is safe, and communicate these safety statuses to the user using a variety of audio and visual interfaces. The user can interact with the system through specifying the size of their “comfort zone”, engaging the turn signal, or by simply driving across lane dividers. Additionally, RASCL provides speed change recommendations that are predicted to turn an unsafe lane change situation into a safe situation and enables communication with other vehicles by automatically controlling the turn signal when the driver attempts to change lanes without using the turn signal.