A traffic monitoring system is an integral part of Intelligent Transportation
Systems (ITS). It is one of the critical transportation infrastructures that
transportation agencies invest a huge amount of money to collect and analyze
the traffic data to better utilize the roadway systems, improve the safety of
transportation, and establish future transportation plans. With recent advances
in MEMS, machine learning, and wireless communication technologies, numerous
innovative traffic monitoring systems have been developed. In this article, we
present a review of state-of-the-art traffic monitoring systems focusing on the
major functionality--vehicle classification. We organize various vehicle
classification systems, examine research issues and technical challenges, and
discuss hardware/software design, deployment experience, and system performance
of vehicle classification systems. Finally, we discuss a number of critical
open problems and future research directions in an aim to provide valuable
resources to academia, industry, and government agencies for selecting
appropriate technologies for their traffic monitoring applications.

本文综述了目前基于 MEMS、机器学习和无线通讯技术的交通监控系统，重点关注交通工具分类，并讨论了硬件 / 软件设计、部署经验和系统性能，以及未来的研究方向和关键问题。

车辆分类的智能交通监测系统：一项调查

Intelligent Traffic Monitoring Systems for Vehicle Classification: A  Survey

Advances in micro-electro-mechanical (MEMS) techniques enable inertial
measurements units (IMUs) to be small, cheap, energy efficient, and widely used
in smartphones, robots, and drones. Exploiting inertial data for accurate and
reliable navigation and localization has attracted significant research and
industrial interest, as IMU measurements are completely ego-centric and
generally environment agnostic. Recent studies have shown that the notorious
issue of drift can be significantly alleviated by using deep neural networks
(DNNs), e.g. IONet. However, the lack of sufficient labelled data for training
and testing various architectures limits the proliferation of adopting DNNs in
IMU-based tasks. In this paper, we propose and release the Oxford Inertial
Odometry Dataset (OxIOD), a first-of-its-kind data collection for
inertial-odometry research, with all sequences having ground-truth labels. Our
dataset contains 158 sequences totalling more than 42 km in total distance,
much larger than previous inertial datasets. Another notable feature of this
dataset lies in its diversity, which can reflect the complex motions of
phone-based IMUs in various everyday usage. The measurements were collected
with four different attachments (handheld, in the pocket, in the handbag and on
the trolley), four motion modes (halting, walking slowly, walking normally, and
running), five different users, four types of off-the-shelf consumer phones,
and large-scale localization from office buildings. Deep inertial tracking
experiments were conducted to show the effectiveness of our dataset in training
deep neural network models and evaluate learning-based and model-based
algorithms. The OxIOD Dataset is available at: this http URL

本研究提出并发布了牛津惯性里程计数据集（OxIOD），这是一种首屈一指的数据收集方式，可用于惯性里程计研究，具有地面实况标签。

OxIOD：深度惯性里程计数据集

OxIOD: The Dataset for Deep Inertial Odometry

Inertial sensors provide crucial feedback for control systems to determine
motional status and make timely, automated decisions. Prior efforts tried to
control the output of inertial sensors with acoustic signals. However, their
approaches did not consider sample rate drifts in analog-to-digital converters
as well as many other realistic factors. As a result, few attacks demonstrated
effective control over inertial sensors embedded in real systems.
This work studies the out-of-band signal injection methods to deliver
adversarial control to embedded MEMS inertial sensors and evaluates consequent
vulnerabilities exposed in control systems relying on them. Acoustic signals
injected into inertial sensors are out-of-band analog signals. Consequently,
slight sample rate drifts could be amplified and cause deviations in the
frequency of digital signals. Such deviations result in fluctuating sensor
output; nevertheless, we characterize two methods to control the output:
digital amplitude adjusting and phase pacing. Based on our analysis, we devise
non-invasive attacks to manipulate the sensor output as well as the derived
inertial information to deceive control systems. We test 25 devices equipped
with MEMS inertial sensors and find that 17 of them could be implicitly
controlled by our attacks. Furthermore, we investigate the generalizability of
our methods and show the possibility to manipulate the digital output through
signals with relatively low frequencies in the sensing channel.

本研究探讨一种名为 “out-of-band signal injection” 的新型攻击方法，实现对惯性传感器的对抗性控制，检测在依赖于惯性传感器的控制系统中可能存在的漏洞，针对 25 个装配 MEMS 惯性传感器的设备测试发现 17 个存在隐式控制风险，并揭示了相应的攻击方法和操作过程。