Details
| Original language | English |
|---|---|
| Pages | 252-257 |
| Number of pages | 6 |
| Publication status | Published - 5 Aug 2016 |
| Event | 2016 IEEE Intelligent Vehicles Symposium (IV) - Gothenburg, Sweden, Götheburg, Sweden Duration: 19 Jun 2016 → 22 Jun 2016 |
Conference
| Conference | 2016 IEEE Intelligent Vehicles Symposium (IV) |
|---|---|
| Country/Territory | Sweden |
| City | Götheburg |
| Period | 19 Jun 2016 → 22 Jun 2016 |
Abstract
Technologies for fully automated driving are currently a hot topic for both industry and academia. To achieve a full automation, self-driving cars need a precise localization module. Most of existing localization approaches consist of two main steps: map generation and actual localization that uses the map obtained at the first step. The localization quality of a system directly depends on the capabilities of its sensors, the quality of a map, as well as correctness and effectiveness with which a localization method uses new observations. Therefore, to provide the best results, such systems are equipped with ever-increasing number of sensors. However, extraction of relevant information from sensor data is still challenging. This paper focuses on two localization components: landmark tracking and fusion. We consider landmarks corresponding to poles and road surface markings. They are extracted from the data provided by four fisheye cameras placed around a car and a front lidar. Detection of landmarks depends on characteristics of a sensor: its quality, delay (time), and output rate (frequency). Therefore, we have developed a tracking and fusion module based on the Sequential Probability Ratio Test which is used for both map generation and localization steps. This module was evaluated in a number of driving tests and the results showed high map quality and low localization error.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Automotive Engineering
- Mathematics(all)
- Modelling and Simulation
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2016. 252-257 Paper presented at 2016 IEEE Intelligent Vehicles Symposium (IV), Götheburg, Sweden.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Multi-sensor tracking with SPRT in an autonomous vehicle.
AU - Stess, Marek
AU - Schildwachter, Christian
AU - Mersheeva, Vera
AU - Ortmeier, Frank
AU - Wagner, Bernardo
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions. Publisher Copyright: © 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/8/5
Y1 - 2016/8/5
N2 - Technologies for fully automated driving are currently a hot topic for both industry and academia. To achieve a full automation, self-driving cars need a precise localization module. Most of existing localization approaches consist of two main steps: map generation and actual localization that uses the map obtained at the first step. The localization quality of a system directly depends on the capabilities of its sensors, the quality of a map, as well as correctness and effectiveness with which a localization method uses new observations. Therefore, to provide the best results, such systems are equipped with ever-increasing number of sensors. However, extraction of relevant information from sensor data is still challenging. This paper focuses on two localization components: landmark tracking and fusion. We consider landmarks corresponding to poles and road surface markings. They are extracted from the data provided by four fisheye cameras placed around a car and a front lidar. Detection of landmarks depends on characteristics of a sensor: its quality, delay (time), and output rate (frequency). Therefore, we have developed a tracking and fusion module based on the Sequential Probability Ratio Test which is used for both map generation and localization steps. This module was evaluated in a number of driving tests and the results showed high map quality and low localization error.
AB - Technologies for fully automated driving are currently a hot topic for both industry and academia. To achieve a full automation, self-driving cars need a precise localization module. Most of existing localization approaches consist of two main steps: map generation and actual localization that uses the map obtained at the first step. The localization quality of a system directly depends on the capabilities of its sensors, the quality of a map, as well as correctness and effectiveness with which a localization method uses new observations. Therefore, to provide the best results, such systems are equipped with ever-increasing number of sensors. However, extraction of relevant information from sensor data is still challenging. This paper focuses on two localization components: landmark tracking and fusion. We consider landmarks corresponding to poles and road surface markings. They are extracted from the data provided by four fisheye cameras placed around a car and a front lidar. Detection of landmarks depends on characteristics of a sensor: its quality, delay (time), and output rate (frequency). Therefore, we have developed a tracking and fusion module based on the Sequential Probability Ratio Test which is used for both map generation and localization steps. This module was evaluated in a number of driving tests and the results showed high map quality and low localization error.
UR - http://www.scopus.com/inward/record.url?scp=84983296820&partnerID=8YFLogxK
U2 - 10.1109/ivs.2016.7535394
DO - 10.1109/ivs.2016.7535394
M3 - Paper
SP - 252
EP - 257
T2 - 2016 IEEE Intelligent Vehicles Symposium (IV)
Y2 - 19 June 2016 through 22 June 2016
ER -