Safe road-crossing by self-driving vehicles is a crucial problem to address
in smart-cities. In this paper, we introduce a multi-sensor fusion approach to
support road-crossing decisions in a system composed by an autonomous
wheelchair and a flying drone featuring a robust sensory system made of diverse
and redundant components. To that aim, we designed an analytical danger
function based on explainable physical conditions evaluated by single sensors,
including those using machine learning and artificial vision. As a
proof-of-concept, we provide an experimental evaluation in a laboratory
environment, showing the advantages of using multiple sensors, which can
improve decision accuracy and effectively support safety assessment. We made
the dataset available to the scientific community for further experimentation.
The work has been developed in the context of an European project named
REXASI-PRO, which aims to develop trustworthy artificial intelligence for
social navigation of people with reduced mobility.

自动驾驶车辆的安全过马路是智能城市中需要解决的重要问题。本文介绍了一种多传感器融合方法，用于支持由自主轮椅和具备多样化和冗余组件的飞行无人机组成的系统中的过马路决策。为此，我们设计了一个基于可解释的物理条件的分析危险函数，通过单个传感器评估这些条件，其中包括使用机器学习和视觉技术的传感器。作为概念证明，我们在实验室环境中进行了实验评估，展示了使用多个传感器的优势，这可以提高决策准确性并有效支持安全评估。我们将数据集提供给了科学界，以进行进一步的实验。这项工作是在一项名为 REXASI-PRO 的欧洲项目的框架下开展的，该项目旨在为行动不便的人们的社交导航开发可信赖的人工智能。

自主轮椅的安全过马路：一种新型数据集及其实验评估

Safe Road-Crossing by Autonomous Wheelchairs: a Novel Dataset and its  Experimental Evaluation

Even though virtual testing of Autonomous Vehicles (AVs) has been well
recognized as essential for safety assessment, AV simulators are still
undergoing active development. One particularly challenging question is to
effectively include the Sensing and Perception (S&P) subsystem into the
simulation loop. In this article, we define Perception Error Models (PEM), a
virtual simulation component that can enable the analysis of the impact of
perception errors on AV safety, without the need to model the sensors
themselves. We propose a generalized data-driven procedure towards parametric
modeling and evaluate it using Apollo, an open-source driving software, and
nuScenes, a public AV dataset. Additionally, we implement PEMs in SVL, an
open-source vehicle simulator. Furthermore, we demonstrate the usefulness of
PEM-based virtual tests, by evaluating camera, LiDAR, and camera-LiDAR setups.
Our virtual tests highlight limitations in the current evaluation metrics, and
the proposed approach can help study the impact of perception errors on AV
safety.

本文提出了感知误差模型（PEM），并针对 Apollo 和 nuScences 两个开源驾驶软件进行了参数建模和评估。通过在 SVL 等开源车辆模拟器中实现 PEM，我们通过评估相机、光达和相机 - 光达设置，展示了基于 PEM 的虚拟测试的实用性，并发现其中的局限性。

自主驾驶车辆中感知误差的模拟

On the Simulation of Perception Errors in Autonomous Vehicles

To operate safely, an automated vehicle (AV) must anticipate how the
environment around it will evolve. For that purpose, it is important to know
which prediction models are most appropriate for every situation. Currently,
assessment of prediction models is often performed over a set of trajectories
without distinction of the type of movement they capture, resulting in the
inability to determine the suitability of each model for different situations.
In this work we illustrate how standardized evaluation methods result in wrong
conclusions regarding a model's predictive capabilities, preventing a clear
assessment of prediction models and potentially leading to dangerous on-road
situations. We argue that following evaluation practices in safety assessment
for AVs, assessment of prediction models should be performed in a
scenario-based fashion. To encourage scenario-based assessment of prediction
models and illustrate the dangers of improper assessment, we categorize
trajectories of the Waymo Open Motion dataset according to the type of movement
they capture. Next, three different models are thoroughly evaluated for
different trajectory types and prediction horizons. Results show that common
evaluation methods are insufficient and the assessment should be performed
depending on the application in which the model will operate.

本文研究了自动驾驶车辆的安全性评估中的预测模型。作者指出，常用的评估方法不足以准确地评估预测模型，应该采用场景评估方法进行评估。通过 Waymo 开放运动数据集的轨迹分类，作者评估了三种不同的预测模型。结果表明，应根据模型所用于的应用程序的要求进行评估。