<label id="xi47v"><meter id="xi47v"></meter></label>

      New system helps self-driving cars predict pedestrian movement

      Source: Xinhua| 2019-02-13 08:25:31|Editor: WX
      Video PlayerClose

      CHICAGO, Feb. 12 (Xinhua) -- Researchers at the University of Michigan (UM) are teaching self-driving cars to recognize and predict pedestrian movements with greater precision by zeroing in on humans' gait, body symmetry and foot placement.

      According to a news released posted on UM's website Tuesday, the researchers captured video snippets of humans in motion in data collected by vehicles through cameras, LiDAR and GPS, and recreated them in 3D computer simulation.

      And based on this, they've created a "biomechanically inspired recurrent neural network" that catalogs human movements, with which they can predict poses and future locations for one or several pedestrians up to about 50 yards from the vehicle, about the scale of a city intersection.

      The results have shown that this new system improves upon a driverless vehicle's capacity to recognize what's most likely to happen next.

      "The median translation error of our prediction was approximately 10 cm after one second and less than 80 cm after six seconds. All other comparison methods were up to 7 meters off," said Matthew Johnson-Roberson, associate professor in UM's Department of Naval Architecture and Marine Engineering. "We're better at figuring out where a person is going to be."

      To rein in the number of options for predicting the next movement, the researchers applied the physical constraints of the human body: human's inability to fly or fastest possible speed on foot.

      "Now, we're training the system to recognize motion and making predictions of not just one single thing, whether it's a stop sign or not, but where that pedestrian's body will be at the next step and the next and the next," said Johnson-Roberson.

      Prior work in the area typically looked only at still images. It wasn't really concerned with how people move in three dimensions, said Ram Vasudevan, UM assistant professor of mechanical engineering.

      By utilizing video clips that run for several seconds, the UM system can study the first half of the snippet to make its predictions, and then verify the accuracy with the second half.

      "We are open to diverse applications and exciting interdisciplinary collaboration opportunities, and we hope to create and contribute to a safer, healthier, and more efficient living environment," said UM research engineer Xiaoxiao Du.

      The study has been published online in IEEE Robotics and Automation Letters, and will appear in a forthcoming print edition.

      TOP STORIES
      EDITOR’S CHOICE
      MOST VIEWED
      EXPLORE XINHUANET
      010020070750000000000000011100901378174941
      主站蜘蛛池模板: 中文字幕亚洲一区| 精品久久洲久久久久护士免费| 亚洲成A人片77777国产| 亚洲中文字幕无码av永久| 黄色网址免费大全| 亚洲日韩中文字幕天堂不卡| 每天更新的免费av片在线观看| 亚洲五月激情综合图片区| 国产白丝无码免费视频| 久久久久亚洲AV片无码| 日韩午夜理论免费TV影院| 亚洲一区二区成人| 久久精品国产免费观看| 亚洲人成片在线观看| 免费观看毛片视频| 污视频网站在线免费看| 亚洲视频在线免费| a级毛片在线免费观看| 亚洲视频在线视频| 日韩中文字幕精品免费一区| 亚洲综合小说另类图片动图| 国产成人无码免费视频97 | 久久免费区一区二区三波多野| 亚洲日韩精品射精日| 84pao强力永久免费高清| 亚洲五月综合缴情婷婷| 国产免费拔擦拔擦8x| 中国内地毛片免费高清| 亚洲AV无码国产精品色午友在线| 国产精品免费AV片在线观看| 亚洲最大成人网色香蕉| 亚洲福利精品一区二区三区| 久久aⅴ免费观看| 亚洲美国产亚洲AV| 亚洲精品乱码久久久久久蜜桃不卡| 午夜精品免费在线观看| 亚洲午夜理论片在线观看| 在线a亚洲v天堂网2019无码| 国产精品视频免费| 免费中文字幕视频| 亚洲欧洲日韩国产|