今日论文推荐（附下载地址）

AMiner下周三将发布《智能机器人研究报告》

届时微信公众号菜单栏可直接下载研究报告

敬请关注

论文名：

Improving the Expressiveness of Deep Learning Frameworks with Recursion

作者：

Ming-Yuan Yu, Ram Vasudevan, Matthew Johnson-Roberson

Abstract

Navigating safely in urban environments remains a challenging problem for autonomous vehicles. Occlusion and limited sensor range can pose significant challenges to safely navigate among pedestrians and other vehicles in the environment. Enabling vehicles to quantify the risk posed by unseen regions allows them to anticipate future possibilities, resulting in increased safety and ride comfort. This paper proposes an algorithm that takes advantage of the known road layouts to forecast, quantify, and aggregate risk associated with occlusions and limited sensor range. This allows us to make predictions of risk induced by unobserved vehicles even in heavily occluded urban environments. The risk can then be used either by a low-level planning algorithm to generate better trajectories, or by a high-level one to plan a better route. The proposed algorithm is evaluated on intersection layouts from real-world map data with up to five other vehicles in the scene, and verified to reduce collision rates by 4.8x comparing to a baseline method while improving driving comfort.

中文摘要

在城市环境中安全驾驶仍然是自动驾驶车辆的一个挑战性问题。闭塞和有限的传感器范围可能对在环境中的行人和其他车辆之间安全导航提出重大挑战。使车辆能够量化不可见区域带来的风险，使他们能够预测未来的可能性，从而提高安全性和乘坐舒适性。本文提出了一种算法，该算法利用已知的道路布局来预测，量化和汇总与遮挡和有限传感器范围相关的风险。这使我们能够预测即使在严重封闭的城市环境中由未观察到的车辆引起的风险。然后，风险可以通过低级规划算法来生成更好的轨迹，或者通过高级计划算法来规划更好的路线。所提出的算法在真实世界地图数据的交叉点布局上与场景中的多达五个其他车辆进行评估，并且在提高驾驶舒适性的同时，与基线方法相比，验证了将碰撞率降低了4.8倍。

论文下载链接：https://arxiv.org/pdf/1809.04629.pdf