车联网：智能交通系统与无线网络应用

Vehicles

车联网

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"Connected Vehicles" 是一本专注于智能交通系统中车联网技术的书籍，由Radovan Miucic编辑，属于无线网络系列。该书探讨了车联网的关键技术和应用，涵盖了2019年的研究进展。在车联网（Connected Vehicles）领域，本书详细讨论了以下几个核心知识点： 1. 定位技术：车辆连接系统中的精确定位是关键，它涉及到GPS和其他卫星导航系统的应用，以及如何在城市环境中克服遮挡和多路径效应以提供准确的车辆位置信息。 2. 人机交互界面（Human Machine Interface, HMI）：HMI在车联网中扮演着至关重要的角色，它确保驾驶员能够安全、有效地与车载系统进行交互，接收和发送相关信息，如路况警告、导航指示等。 3. 安全证书管理系统（Security Credential Management System, SCMS）：在V2X（车对万物）通信中，SCMS保障了数据的安全传输，防止恶意攻击和身份伪造，这对于车与车（V2V）、车与基础设施（V2I）之间的通信至关重要。 4. 车对车（V2V）安全通信：这部分内容深入分析了V2V通信协议和技术，如何通过无线通信传递实时安全信息，如碰撞预警、驾驶行为监测等，以提升道路安全。 5. 车与基础设施（V2I）通信：V2I通信涉及到车辆与交通信号、路侧单元等基础设施的数据交换，以优化交通流、减少拥堵并提高交通效率。 6. 协同式车对行人（V2P）安全系统：这一部分讨论了如何利用车联网技术保护行人的安全，例如通过预测行人的动态，提前警告驾驶员潜在危险。 7. 5.9 GHz频谱共享：5.9 GHz频段是车联网的主要频谱，书中可能探讨了如何有效地利用这个频段，同时考虑与其他无线服务的共存和干扰管理。 8. 高效高保真DSRC（Dedicated Short-Range Communications）模拟：DSRC是一种用于V2X通信的技术，高效的模拟工具对于理解和优化其性能至关重要。 9. 车联网在自动驾驶中的应用：随着自动驾驶技术的发展，车联网技术在实现自主决策、路径规划和环境感知方面的作用日益凸显。这些章节共同构建了一个全面的车联网知识框架，不仅涵盖了技术基础，还深入到实际应用和安全问题，对于理解当前和未来智能交通系统的潜力具有重要价值。通过这本书，读者可以了解到车联网领域的最新进展和挑战，以及它们对交通安全、效率和自动化驾驶的影响。

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10 R. Miucic

we propose a comprehensive system design to improve the positioning of an

ego vehicle based on Kalman ﬁlters. In this approach, the ego vehicle fuses its

own position information obtained by the on-board GPS receiver with position

information of nearby vehicles collected by the on-board ranging sensor(s) and the

messages received via the DSRC transceiver from other equipped vehicles. This

process also involves performing track matching using a multi-sensor multi-target

track association algorithm. On the other hand, the second application aims at road

geometry estimation as an essential step in ADAS applications where an ego vehicle

builds a local map of the road ahead using its on-board sensors. We propose a novel

design for road geometry estimation by fusing on-board sensor (camera and radar)

data with the standard V2V messages received from remote vehicles via DSRC.

Our Kalman Filter-based methods fuse measurements by on-board sensors with

information from V2V messages to produce a long-range estimate for the road

geometry ahead of the ego vehicle. For the localization application, we provide

insights on the system design and present simulation and experimental results that

show signiﬁcant performance gains of the proposed methods in terms of localization

accuracy and matching accuracy. For the road geometry estimation application, we

show by experimental data that the proposed method achieves more than 7× the

accuracy of current state-of-the-art camera-radar fusion methods.

12 Z. Popovic and R. Miucic

Positioning Requirements for Intelligent Transportation Systems

For sharing of position data to be effective in improving safety, it needs to be of

certain accuracy and reliability. There are some trafﬁc scenarios in which road-level

positioning accuracy—knowing which road we are on—can be sufﬁcient to provide

assessments needed for issuing warnings and corrective actions that can improve

safety. Examples include dealing with oncoming trafﬁc in making unprotected turns

through intersections and slowing down for road conditions ahead (tight turns seen

in maps, icy spots reported by other vehicles, ...). Beyond that, there is a signiﬁcant

portion of trafﬁc scenarios in which reliably knowing positions of trafﬁc participants

down to the lane of travel is needed (and sufﬁcient) to prevent collisions or road

excursions. Among others these include: anticipating sudden trafﬁc slow-downs

in our own lane, preventing unintended lane or road departures, and reacting to

trafﬁc controls applicable only to our lane, where the latter can depend on similarly

accurate maps of intersection zones.

With the aim of quantifying the required positioning accuracy levels, road-level

accuracy values are derived from the need to distinguish between nearby or adjacent

roads that have similar directions of travel but are not physically or topologically

connected and thus can’t inﬂuence each other’s trafﬁc. This distinction is needed

to prevent false warnings and reactions. In the most stringent and ambiguous

cases, for example adjacent lanes separated by a barrier, this requirement reduces

to the requirement of lane-level accuracy, but often 5 or 10 m of accuracy is

sufﬁcient.

Setting a number for lane-level accuracy also does not have a deﬁnite anchor, but

1.5 m is suggested here because for a vehicle travelling down the middle of a 3 m

lane any larger error would place it in another (and thus wrong) lane. A 3 m lane,

which is an unusually narrow lane in the United State, is used here in order to cover

most of the lane width diversity: In the US, lanes with widths of 3 m or more are

used about 90% of the time [1].

The main source of positioning information in V2X systems, and the only source

of absolute positioning information, has been GPS (Global Positioning System,

maintained by the United States government) and similar systems maintained by

other governments, where any such system is known as a GNSS system (Global

Navigation Satellite System). GNSS can provide adequate positioning performance

for V2X in most cases, but to make a positioning system sufﬁciently robust for

safety applications it needs to be combined with other information, typically from

sensors that reliably relate a vehicle’s change of position over time (such as inertial

sensors), but can’t provide absolute position coordinates (which are required for

seamless sharing of location with other vehicles and for use of maps).

The aims of this chapter are to introduce: operation of GNSS systems while using

GPS as the main example (Section “GNSS Principles”), use of GNSS positioning

in V2X systems (Section “Basic GNSS Positioning in Cooperative Vehicles”),

GNSS sources of errors and ways of limiting those errors, including advanced

techniques for achieving centimeter-level accuracy (Section “GNSS Performance

Positioning 13

and High-Accuracy Methods”), and ways of combing GNSS data with other

sensing for achieving a robust position solution (Section “Multi-Sensor Fusion

for Robust and Accurate Positioning”). The goal of covering this broad scope

in a single introductory chapter does not allow room for getting into the depth

of theoretical derivations and implementation details, but those are already well

covered in existing dedicated publications and the value of this chapter instead lies

in enabling those new to these ﬁelds to quickly become familiar with numerous

key concepts through an intuitively developed understanding. This serves to allow

effective initial consideration of the technologies involved and can efﬁciently direct

further independent study.

GNSS Principles

What Is GPS?

GPS, strictly speaking, stands for Global Positioning System. It is the United States

maintained system that consists of a set of Earth-orbiting satellites. The satellites

continually broadcast signals that allow an Earth-based user with a suitable device

(a GPS receiver) to determine the user location coordinates on Earth typically

to an accuracy in the order of meters, and down to centimeters when locally-

speciﬁc corrective data is available. In this proper sense, which will be used in

this chapter, a GPS receiver that does not contain maps and does not provide

navigation direction. In popular use however, in particular in the United States, the

meaning of “GPS” has been extended to refer to any device that provides displays of

user’s global location on a map and that might also provide turn-by-turn directions,

which elsewhere in the world is more tellingly referred to as “satellite navigation”

or “sat-nav”.

There are similar systems maintained, or in process of development, by other

countries. Such a system is generically referred to as a Global Navigation Satellite

System (GNSS). Characteristics of other GNSS systems are presented in Section

“Other GNSS Systems”.

The discussion that follows is presented as GPS speciﬁc, but in most cases, it

applies to all other GNSS systems. Any important differences will be speciﬁcally

noted.

Trilateration and Triangulation Concepts

The method that underlies GPS (and GNSS) location determination is trilateration,

which is an estimation of user position based on user measurements of distances

to objects with known locations. Sometimes this estimation is mistakenly referred

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