Inspired by human vision, we propose a new periphery-fovea multi-resolution
driving model that predicts vehicle speed from dash camera videos. The
peripheral vision module of the model processes the full video frames in low
resolution. Its foveal vision module selects sub-regions and uses
high-resolution input from those regions to improve its driving performance. We
train the fovea selection module with supervision from driver gaze. We show
that adding high-resolution input from predicted human driver gaze locations
significantly improves the driving accuracy of the model. Our periphery-fovea
multi-resolution model outperforms a uni-resolution periphery-only model that
has the same amount of floating-point operations. More importantly, we
demonstrate that our driving model achieves a significantly higher performance
gain in pedestrian-involved critical situations than in other non-critical
situations.

本文提出了一种基于人眼视觉的全景 - 中央凹多尺度驾驶模型，通过预测司机注视点的位置来改进驾驶性能，实现了在行人所涉及的危急情况下驾驶准确度的显著提高。

人类视觉注意引导的周边 - 中央多分辨率驾驶模型

Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention

Current end-to-end deep learning driving models have two problems: (1) Poor
generalization ability of unobserved driving environment when diversity of
training driving dataset is limited (2) Lack of accident explanation ability
when driving models don't work as expected. To tackle these two problems,
rooted on the believe that knowledge of associated easy task is benificial for
addressing difficult task, we proposed a new driving model which is composed of
perception module for \textit{see and think} and driving module for
\textit{behave}, and trained it with multi-task perception-related basic
knowledge and driving knowledge stepwisely.
Specifically segmentation map and depth map (pixel level understanding of
images) were considered as \textit{what \& where} and \textit{how far}
knowledge for tackling easier driving-related perception problems before
generating final control commands for difficult driving task. The results of
experiments demonstrated the effectiveness of multi-task perception knowledge
for better generalization and accident explanation ability. With our method the
average sucess rate of finishing most difficult navigation tasks in untrained
city of CoRL test surpassed current benchmark method for 15 percent in trained
weather and 20 percent in untrained weathers. Demonstration video link is:
this https URL

本文提出了一个基于分析易任务知识对困难任务进行指导以增强驾驶模型泛化力和事故解释能力的新型驾驶模型，通过多任务感知相关基础知识和驾驶知识的逐步训练，成功地提高了驾驶模型的平均完成率，使其在未训练的城市和天气中大幅优于当前基准方法。

自动驾驶再思考：多任务知识提升泛化及事故解释能力

Rethinking Self-driving: Multi-task Knowledge for Better Generalization  and Accident Explanation Ability

For human drivers, having rear and side-view mirrors is vital for safe
driving. They deliver a more complete view of what is happening around the car.
Human drivers also heavily exploit their mental map for navigation.
Nonetheless, several methods have been published that learn driving models with
only a front-facing camera and without a route planner. This lack of
information renders the self-driving task quite intractable. We investigate the
problem in a more realistic setting, which consists of a surround-view camera
system with eight cameras, a route planner, and a CAN bus reader. In
particular, we develop a sensor setup that provides data for a 360-degree view
of the area surrounding the vehicle, the driving route to the destination, and
low-level driving maneuvers (e.g. steering angle and speed) by human drivers.
With such a sensor setup we collect a new driving dataset, covering diverse
driving scenarios and varying weather/illumination conditions. Finally, we
learn a novel driving model by integrating information from the surround-view
cameras and the route planner. Two route planners are exploited: 1) by
representing the planned routes on OpenStreetMap as a stack of GPS coordinates,
and 2) by rendering the planned routes on TomTom Go Mobile and recording the
progression into a video. Our experiments show that: 1) 360-degree
surround-view cameras help avoid failures made with a single front-view camera,
in particular for city driving and intersection scenarios; and 2) route
planners help the driving task significantly, especially for steering angle
prediction.

通过一个由 8 个摄像头和路线规划器组成的组合实现自动驾驶， Collect driving data on various scenarios and weather conditions and learn a driving model by integrating information from the cameras and the route planner, which can significantly improve steering angle prediction.