A vehicle driving along the road is surrounded by many objects, but only a
small subset of them influence the driver's decisions and actions. Learning to
estimate the importance of each object on the driver's real-time
decision-making may help better understand human driving behavior and lead to
more reliable autonomous driving systems. Solving this problem requires models
that understand the interactions between the ego-vehicle and the surrounding
objects. However, interactions among other objects in the scene can potentially
also be very helpful, e.g., a pedestrian beginning to cross the road between
the ego-vehicle and the car in front will make the car in front less important.
We propose a novel framework for object importance estimation using an
interaction graph, in which the features of each object node are updated by
interacting with others through graph convolution. Experiments show that our
model outperforms state-of-the-art baselines with much less input and
pre-processing.

本文提出一种用交互图和图卷积进行物体重要性估计的新框架，以帮助更好地理解人类驾驶行为并实现更可靠的自动驾驶系统。实验结果表明，该模型具有比现有基线模型更好的性能。

道路行驶视频中物体重要性估计的交互图

Interaction Graphs for Object Importance Estimation in On-road Driving  Videos

The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.

采用生成式对抗模仿学习模型训练递归策略，提高了人类驾驶行为仿真在智能交通系统应用中的准确性，具有较高的实用性。