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 -