For robotic vehicles to navigate safely and efficiently in pedestrian-rich
environments, it is important to model subtle human behaviors and navigation
rules (e.g., passing on the right). However, while instinctive to humans,
socially compliant navigation is still difficult to quantify due to the
stochasticity in people's behaviors. Existing works are mostly focused on using
feature-matching techniques to describe and imitate human paths, but often do
not generalize well since the feature values can vary from person to person,
and even run to run. This work notes that while it is challenging to directly
specify the details of what to do (precise mechanisms of human navigation), it
is straightforward to specify what not to do (violations of social norms).
Specifically, using deep reinforcement learning, this work develops a
time-efficient navigation policy that respects common social norms. The
proposed method is shown to enable fully autonomous navigation of a robotic
vehicle moving at human walking speed in an environment with many pedestrians.

使用深度强化学习，本文研发了一种尊重人们常见社交规范的导航策略，可以使机器人车辆在行人密集的环境中以人类步速进行完全自主导航。

具有深度强化学习的社会感知运动规划

Socially Aware Motion Planning with Deep Reinforcement Learning

In this paper we present and analyze a queueing-theoretical model for
autonomous mobility-on-demand (MOD) systems where robotic, self-driving
vehicles transport customers within an urban environment and rebalance
themselves to ensure acceptable quality of service throughout the entire
network. We cast an autonomous MOD system within a closed Jackson network model
with passenger loss. It is shown that an optimal rebalancing algorithm
minimizing the number of (autonomously) rebalancing vehicles and keeping
vehicles availabilities balanced throughout the network can be found by solving
a linear program. The theoretical insights are used to design a robust,
real-time rebalancing algorithm, which is applied to a case study of New York
City. The case study shows that the current taxi demand in Manhattan can be met
with about 8,000 robotic vehicles (roughly 60% of the size of the current taxi
fleet). Finally, we extend our queueing-theoretical setup to include congestion
effects, and we study the impact of autonomously rebalancing vehicles on
overall congestion. Collectively, this paper provides a rigorous approach to
the problem of system-wide coordination of autonomously driving vehicles, and
provides one of the first characterizations of the sustainability benefits of
robotic transportation networks.

本研究提出并分析一个排队论模型，用于自主移动出行（MOD）系统，其中机器人、自动驾驶车辆在城市环境中运输乘客并使自身再平衡以确保整个网络的可接受服务质量。研究表明，可以通过解决线性规划来找到最优再平衡算法，从而最小化（自主）再平衡车辆数量并保持整个网络车辆可用性的平衡。我们基于这些理论洞见设计了一个稳健的实时再平衡算法，并将其应用于纽约市的案例研究。这篇论文提供了一个解决自主驾驶车辆系统范围协调问题的严谨方法，并提供了对机器人交通网络可持续性益处的首个表征。