In this paper, we present a novel digital twin prototype for a
learning-enabled self-driving vehicle. The primary objective of this digital
twin is to perform traffic sign recognition and lane keeping. The digital twin
architecture relies on co-simulation and uses the Functional Mock-up Interface
and SystemC Transaction Level Modeling standards. The digital twin consists of
four clients, i) a vehicle model that is designed in Amesim tool, ii) an
environment model developed in Prescan, iii) a lane-keeping controller designed
in Robot Operating System, and iv) a perception and speed control module
developed in the formal modeling language of BIP (Behavior, Interaction,
Priority). These clients interface with the digital twin platform,
PAVE360-Veloce System Interconnect (PAVE360-VSI). PAVE360-VSI acts as the
co-simulation orchestrator and is responsible for synchronization,
interconnection, and data exchange through a server. The server establishes
connections among the different clients and also ensures adherence to the
Ethernet protocol. We conclude with illustrative digital twin simulations and
recommendations for future work.

本文提出了一个新颖的数字孪生原型，用于学习能力的自动驾驶车辆，其主要目标是进行交通标志识别和车道保持。数字孪生架构依赖于联合仿真，采用了功能模拟接口和 SystemC 事务级建模标准。数字孪生由四个客户端组成，即 i）在 Amesim 工具中设计的车辆模型，ii）在 Prescan 中开发的环境模型，iii）在机器人操作系统中设计的车道保持控制器，以及 iv）在 BIP（行为、交互、优先级）的形式建模语言中开发的感知和速度控制模块。这些客户端与数字孪生平台 PAVE360-Veloce System Interconnect（PAVE360-VSI）进行接口。PAVE360-VSI 充当联合仿真协调器，负责通过服务器进行同步、互连和数据交换。服务器在不同客户端之间建立连接，并确保遵守以太网协议。最后，我们给出了数字孪生仿真和未来工作的建议。

一种基于学习的自主车辆交通标志识别的数字孪生原型

A Digital Twin prototype for traffic sign recognition of a  learning-enabled autonomous vehicle

It can be difficult to autonomously produce driver behavior so that it
appears natural to other traffic participants. Through Inverse Reinforcement
Learning (IRL), we can automate this process by learning the underlying reward
function from human demonstrations. We propose a new IRL algorithm that learns
a goal-conditioned spatiotemporal reward function. The resulting costmap is
used by Model Predictive Controllers (MPCs) to perform a task without any
hand-designing or hand-tuning of the cost function. We evaluate our proposed
Goal-conditioned SpatioTemporal Zeroing Maximum Entropy Deep IRL (GSTZ)-MEDIRL
framework together with MPC in the CARLA simulator for autonomous driving, lane
keeping, and lane changing tasks in a challenging dense traffic highway
scenario. Our proposed methods show higher success rates compared to other
baseline methods including behavior cloning, state-of-the-art RL policies, and
MPC with a learning-based behavior prediction model.

本文提出了一种新的 IRL 算法，通过学习人类示范的目标条件下的时空奖励函数，生成适合用于 MPC 的费用地图，以实现自动驾驶、车道保持和车道变换任务，并在 CARLA 模拟器中进行了测试。结果表明，相比于行为克隆、现有 RL 策略和基于学习的行为预测模型的 MPC 等基准方法，我们提出的方法具有更高的成功率。

通过深度逆强化学习实现 MPC 的时空代价地图推断

Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning

As part of a complete software stack for autonomous driving, NVIDIA has
created a neural-network-based system, known as PilotNet, which outputs
steering angles given images of the road ahead. PilotNet is trained using road
images paired with the steering angles generated by a human driving a
data-collection car. It derives the necessary domain knowledge by observing
human drivers. This eliminates the need for human engineers to anticipate what
is important in an image and foresee all the necessary rules for safe driving.
Road tests demonstrated that PilotNet can successfully perform lane keeping in
a wide variety of driving conditions, regardless of whether lane markings are
present or not.
The goal of the work described here is to explain what PilotNet learns and
how it makes its decisions. To this end we developed a method for determining
which elements in the road image most influence PilotNet's steering decision.
Results show that PilotNet indeed learns to recognize relevant objects on the
road.
In addition to learning the obvious features such as lane markings, edges of
roads, and other cars, PilotNet learns more subtle features that would be hard
to anticipate and program by engineers, for example, bushes lining the edge of
the road and atypical vehicle classes.

NVIDIA 的整个自动驾驶软件中的神经网络系统 PilotNet 能够根据道路前方的图像输出转向角，其根据观测到的人类驾驶员的行为进行训练。本文旨在解释 PilotNet 学习的内容以及其如何作出决策，通过实现确定道路图像中最影响 PilotNet 转向决策的元素的方法，展示 PilotNet 不仅学习了显而易见的车道标记、路边的边缘和其他车辆等特性，还学会了更加微妙难以预料和规划的特征，例如路边的灌木丛和非典型车辆类别。