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 this https URL.

通过将激光扫描仪和常规相机流在特征级别上融合，以克服泛化和一致性挑战，并引入新的高效实用的栅格占据映射方法及其相关算法，从而动态检测道路的不同障碍，并通过指南针指引可控车辆到另一条路线上以到达目的地。该方法在 CARLA 模拟器城市驾驶基准测试中得到了证明，具有显著效果提升。