Ineffective and inflexible traffic signal control at urban intersections can
often lead to bottlenecks in traffic flows and cause congestion, delay, and
environmental problems. How to manage traffic smartly by intelligent signal
control is a significant challenge in urban traffic management. With recent
advances in machine learning, especially reinforcement learning (RL), traffic
signal control using advanced machine learning techniques represents a
promising solution to tackle this problem. In this paper, we propose a RL
approach for traffic signal control at urban intersections. Specifically, we
use neural networks as Q-function approximator (a.k.a. Q-network) to deal with
the complex traffic signal control problem where the state space is large and
the action space can be discrete. The state space is defined based on real-time
traffic information, i.e. vehicle position, direction and speed. The action
space includes various traffic signal phases which are critical in generating a
reasonable and realistic control mechanism, given the prominent
spatial-temporal characteristics of urban traffic. In the simulation
experiment, we use SUMO, an open source traffic simulator, to construct
realistic urban intersection settings. Moreover, we use different traffic
patterns, such as major/minor road traffic, through/left-turn lane traffic,
tidal traffic, and varying demand traffic, to train a generalized traffic
signal control model that can be adapted to various traffic conditions. The
simulation results demonstrate the convergence and generalization performance
of our RL approach as well as its significant benefits in terms of queue length
and wait time over several benchmarking methods in traffic signal control.

本文基于强化学习方法提出一种针对城市交叉口交通信号控制的新模型，采用神经网络作为 Q 函数的近似器进行复杂的信号控制，使用开放源代码的交通模拟器 SUMO 进行实验，结果表明该模型能够显著降低排队长度和等待时间，与基准方法相比具有更好的收敛性和泛化性能。

城市交叉口智能交通信号控制的强化学习方法

A Reinforcement Learning Approach for Intelligent Traffic Signal Control  at Urban Intersections

This paper presents a novel dataset titled PedX, a large-scale multimodal
collection of pedestrians at complex urban intersections. PedX consists of more
than 5,000 pairs of high-resolution (12MP) stereo images and LiDAR data along
with providing 2D and 3D labels of pedestrians. We also present a novel 3D
model fitting algorithm for automatic 3D labeling harnessing constraints across
different modalities and novel shape and temporal priors. All annotated 3D
pedestrians are localized into the real-world metric space, and the generated
3D models are validated using a mocap system configured in a controlled outdoor
environment to simulate pedestrians in urban intersections. We also show that
the manual 2D labels can be replaced by state-of-the-art automated labeling
approaches, thereby facilitating automatic generation of large scale datasets.

本文介绍了一个名为 PedX 的新数据集，它是一个大规模的多模态数据集，包含超过 5,000 个高分辨率（12MP）的立体图像和 LiDAR 数据，并提供行人的 2D 和 3D 标签。我们还提出了一种新颖的 3D 模型拟合算法，用于自动化 3D 标记，利用不同模式和新颖形状和时间先验的约束。同时，我们还展示了手动 2D 标签可以被最先进的自动标记方法替换，从而促进大规模数据集的自动生成。在控制的户外环境中配置 mocap 系统以模拟城市交叉口的行人时，我们还将所有标注的 3D 行人定位到实际世界度量空间，并使用此系统验证了生成的 3D 模型。

PedX：在复杂城市交叉口中进行行人度量 3D 姿态估计的基准数据集

PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in  Complex Urban Intersections

Understanding the intentions of drivers at intersections is a critical
component for autonomous vehicles. Urban intersections that do not have traffic
signals are a common epicentre of highly variable vehicle movement and
interactions. We present a method for predicting driver intent at urban
intersections through multi-modal trajectory prediction with uncertainty. Our
method is based on recurrent neural networks combined with a mixture density
network output layer. To consolidate the multi-modal nature of the output
probability distribution, we introduce a clustering algorithm that extracts the
set of possible paths that exist in the prediction output, and ranks them
according to likelihood. To verify the method's performance and
generalizability, we present a real-world dataset that consists of over 23,000
vehicles traversing five different intersections, collected using a vehicle
mounted Lidar based tracking system. An array of metrics is used to demonstrate
the performance of the model against several baselines.

使用循环神经网络和混合密度网络在城市路口进行多模态路径预测和聚类，验证了方法的性能和广泛适用性。