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文章目录

前言
一、Cooperative Vehicle Positioning via V2V Communications and Onboard Sensors
- 1.Abstract
- 2. introduction
- 3.step：
- - 1.Preliminaries
  - 2.Cooperative positioning: principle and examples
  - 3. DESIGN DETAILS
  - 4.PERFORMANCE EVALUATION
- 4. CONCLUSION

前言

提示：本文是一些车路协同定位的英文文献的总结笔记

一、Cooperative Vehicle Positioning via V2V Communications and Onboard Sensors

tips：协同车辆定位通过V2V通信和机载传感器（主要是针对v2v和车载传感器）2011/09

1.Abstract

This paper presents a vehicular positioning system in which multiple vehicles cooperatively calibrate their positions and recognize surrounding vehicles with their GPS receivers and ranging sensors. The proposed system operates in a distributed manner and works even if all vehicles nearby do not or cannot participate in the system. Each vehicle acquires various pieces of positioning information with different degrees of accuracies depending on the sources and recency of information, and compiles them based on likelihood derived from estimated accuracies to minimize estimation errors. A simulation based performance evaluation given in the paper shows that the proposed system improves the estimation accuracy by 85% on average with respect to the standalone GPS receiver, and recognizes about 70% surrounding vehicles with an error of 1m.
分布式车辆定位系统，多辆车协同标定其位置，并利用其GPS接收器和测距传感器识别周围车辆。每个车辆根据信息的来源和最近（上次的）获取具有不同精确度的各种定位信息，并且基于从估计精确度导出的似然性来估计预测它们，以最小化估计误差。
基于仿真的性能评估表明，相对于独立的全球定位系统接收机，该系统的估计精度平均提高了85%，并且能够识别大约70%的周围车辆，误差为1米。

2. introduction

positioning errors introduced by GPS receivers can be several times larger than that in urban areas with many obstacles to GPS receivers
（1）Some methods assume additional hardware such as Differential GPS, gyroscopes and acceleration sensors, and fuse the information to improve the position accuracy
（2）some other methods estimate relative positions of vehicles originating from position of a vehicle using information shared among vehicles via V2V communication
（3）In addition, some methods have been proposed to estimate driving lanes of vehicles by using onboard sensors and V2V communication
These existing methods can achieve sufficient accuracy to some ITS applications such as car navigation systems. However, it is difficult to satisfy more severe requirements in vehicle safety applications.（满足精度，不满足安全）
（4）some automotive companies commercialize safety systems such as Volvo’s Collision Warning with Auto Brake and Toyota’s Pre-Collision System
based on situation awareness. They utilize distances from surrounding obstacles obtained by ranging sensors such as millimeter wave radar and laser sensors. Some researches have also been proposed to improve situation awareness of vehicles using ranging sensors and V2V communications [11]. However, they do not consider how to share the information among vehicles to improve the recognition and position accuracy.
（一些汽车公司将安全系统商业化，如沃尔沃的自动刹车碰撞警告系统和丰田的基于情况意识的碰撞前系统。它们利用距离周围障碍物的距离，这些距离是由毫米波雷达和激光传感器等测距传感器获得的。还提出了一些研究来使用测距传感器和V2V通信来提高车辆的态势感知[11]。然而，他们没有考虑如何在车辆之间共享信息以提高识别和定位精度。）

假设：
本文假设车辆都载有GPS，测距传感器，如毫米波雷达传感器和DSRC/WAVE通信设备。每辆车都与周围的车辆共享来自GPS和测距传感器的测量值，并使用不同车辆在不同时间的测量值更新位置。
We assume that some vehicles hold GPS receivers and ranging sensors such as millimeter wave radar sensors and DSRC/WAVE communication devices. Each vehicle shares measurements from GPS and ranging sensors with surrounding vehicles, and updates positions using the measurements by different vehicles at different times.

结论：为了减轻由随时间衰减引起的具有大误差的测量的影响，车辆估计每次测量的“精度”，并通过参考它来估计位置。此外，车辆基于中心极限定理估计估计位置的“准确性”，以与周围车辆共享最准确的位置。从性能评估中，我们确认我们的系统可以将车辆的位置误差比独立的GPS接收器平均减少85%，并且可以识别大约70%的周围车辆，误差为1米。
In order to mitigate the impact of the measurement with large error caused by decay with time, a vehicle estimates the “accuracy” for each measurement, and estimates the positions by reference to it. Also, a vehicle estimates the “accuracy” of estimated positions based on the Central Limit Theorem to share the most accurate positions with surrounding vehicles. From performance evaluation, we confirm that our system could reduce position error of vehicles by 85% on average from that of the standalone GPS receiver, and recognize about 70% of all surrounding vehicles with an error of 1m.

3.step：

1.Preliminaries

做一些必要的假设

2.Cooperative positioning: principle and examples

每个车保存附近车辆的数据，实时更新共享，来估计自己和其他人的位置
Each equipped vehicle holds positions of nearby vehicles,
and updates them every time slot. In order to update the
positions, each vehicle detects its surrounding vehicles as well
as its own GPS position and velocity. This information is
transmitted via a Basic Safety Message to its nearby vehicles.
On receiving each other’s GPS positions and relative positions,
each equipped vehicle estimates the current positions of its own
nearby vehicles.
该方法的目标是允许装备车辆(识别非装备车辆的存在，以及(ii)比GPS和非装备车辆更精确地估计装备车辆的位置。为了实现第二个目标，每个装备的车辆使用来自不同车辆的多边距离测量(即由距离传感器带来的相对位置信息)。这使得能够使用附近车辆的全球定位系统位置作为“锚”，并且多边定位减轻了这些“锚”最初包含的全球定位系统误差。此外，可以探索这种多边定位来探测未装备的车辆，以实现第一个目标。然而，由于位置估计的异步和分布式执行，每个配备的车辆识别的**“车辆地图”可能不同于其他**车辆。
The positioning consists of the following three steps: (1)
obtaining observations (GPS and range measurement), (2)
updating estimation from observations and (3) exchanging
messages.

3. DESIGN DETAILS

A. Obtaining observation (GPS and range measurement)
B. Updating estimation from observations
C. Message exchange

4.PERFORMANCE EVALUATION

A. Simulation settings
B. Simulation results

4. CONCLUSION

This paper has proposed a cooperative vehicle positioning system which provides accurate positions for ITS applications in real-time under the situation where some vehicles have GPS receivers and ranging sensors such as millimeter wave radar sensors and DSRC/WAVE communication devices. From performance evaluation, we confirmed that our system could reduce position errors of vehicles for average 85% and recognize 70% of all nearby vehicles with an error of less 1m. As our future work, we are planning to evaluate the performance of our system by using realistic scenarios.
本文提出了一种协同车辆定位系统，该系统在某些车辆具有全球定位系统接收机和测距传感器(如毫米波雷达传感器和DSRC/WAVE通信设备)的情况下，为智能交通系统的实时应用提供精确的定位。从性能评估中，我们确认我们的系统可以平均减少85%的车辆位置误差，并识别70%误差小于1米的所有附近车辆。作为我们未来的工作，我们计划通过使用现实场景来评估我们系统的性能。

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