Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner, to mimic human driving. This approach has demonstrated suitable vehicle control when following roads, avoiding obstacles, or taking specific turns at intersections to reach a destination. Unfortunately, performance dramatically decreases when deployed to unseen environments and is inconsistent against varying weather conditions. Most importantly, the current CIL fails to avoid static road blockages. In this work, we propose a solution to those deficiencies. First, we fuse the laser scanner with the regular camera streams, at the features level, to overcome the generalization and consistency challenges. Second, we introduce a new efficient Occupancy Grid Mapping (OGM) method along with new algorithms for road blockages avoidance and global route planning. Consequently, our proposed method dynamically detects partial and full road blockages, and guides the controlled vehicle to another route to reach the destination. Following the original CIL work, we demonstrated the effectiveness of our proposal on CARLA simulator urban driving benchmark. Our experiments showed that our model improved consistency against weather conditions by four times and autonomous driving success rate generalization by 52%. Furthermore, our global route planner improved the driving success rate by 37%. Our proposed road blockages avoidance algorithm improved the driving success rate by 27%. Finally, the average kilometers traveled before a collision with a static object increased by 1.5 times. The main source code can be reached at https://heshameraqi.github.io/dynamic_cil_autonomous_driving.
翻译:模拟性模仿学习(CIL) 培训深层神经网络, 以端到端的方式模拟人类驾驶。 这种方法在跟踪道路、 避免障碍、 或在交叉路口进行特定转折以到达目的地时, 展示了适当的车辆控制。 不幸的是, 性能在被部署到隐形环境中时会急剧下降, 且与不同的天气条件不相符。 最重要的是, 目前的CIL未能避免静态的道路阻塞。 在这项工作中, 我们提出了解决这些缺陷的办法 。 首先, 我们将激光扫描器与常规摄像头流在功能层面连接起来, 以克服一般化和一致性的挑战 。 其次, 我们采用了新的高效的自动自动定位网绘图(OGM) 方法, 以及新的道路阻塞和全球路线规划方法 。 因此, 我们提出的方法能动态地探测部分和全部的道路阻塞, 并引导受控车辆前往另一条路段。 在CARIA 模拟城市驱动下, 我们展示了我们关于 CARLA 静性图像的建议的有效性。 我们的实验表明, 我们的模型比天气状况更加一致, 4次, 和自主驱动性驱动目标定位目标测测测测测测测测, 通过主路总路速度率 以5% 成功率 以总路率 提高率 。 通过我们提出的成功率 改进了我们提出的总路路程计划 改进了成功率