Lane map building and localization for automated driving using 2D laser rangefinder

Kim, Dong-Wook; Chung, Tae-Young; Yi, Kyongsu

doi:10.1109/ivs.2015.7225763

Cited by 36 publications

(17 citation statements)

References 13 publications

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“…This can be solved by fusing LiDAR data with cameras able to perceive non reflective lane marks [63]. Some works use a 2D LiDAR sensor to extract road geometry and road marks [72,73].…”

Section: ) Lidar Based Solutionsmentioning

confidence: 99%

A Review of Sensor Technologies for Perception in Automated Driving

Martí

Miguel

Fernández

et al. 2019

IEEE Intell. Transport. Syst. Mag.

238

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After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles.

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“…This can be solved by fusing LiDAR data with cameras able to perceive non reflective lane marks [63]. Some works use a 2D LiDAR sensor to extract road geometry and road marks [72,73].…”

Section: ) Lidar Based Solutionsmentioning

confidence: 99%

A Review of Sensor Technologies for Perception in Automated Driving

Martí

Miguel

Fernández

et al. 2019

IEEE Intell. Transport. Syst. Mag.

238

View full text Add to dashboard Cite

show abstract

“…The feature-level approach utilizes features of the road marking extracted from raw data of the perception sensor. Hata and Wolf [13], Kim et al [14], and Suganuma and Uozumi [15] find road marking features by comparing infrared reflectivities acquired by LIDARs with neighboring reflectivities or threshold values. Schreiber et al [16] extracts road marking features by applying the oriented matched filter to free space obtained by a stereo camera.…”

Section: Related Researchmentioning

confidence: 99%

Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways

Jang

Suhr

Jung

2018

Sensors

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Landmark-based vehicle localization is a key component of both autonomous driving and advanced driver assistance systems (ADAS). Previously used landmarks in highways such as lane markings lack information on longitudinal positions. To address this problem, lane endpoints can be used as landmarks. This paper proposes two essential components when using lane endpoints as landmarks: lane endpoint detection and its accuracy evaluation. First, it proposes a method to efficiently detect lane endpoints using a monocular forward-looking camera, which is the most widely installed perception sensor. Lane endpoints are detected with a small amount of computation based on the following steps: lane detection, lane endpoint candidate generation, and lane endpoint candidate verification. Second, it proposes a method to reliably measure the position accuracy of the lane endpoints detected from images taken while the camera is moving at high speed. A camera is installed with a mobile mapping system (MMS) in a vehicle, and the position accuracy of the lane endpoints detected by the camera is measured by comparing their positions with ground truths obtained by the MMS. In the experiment, the proposed methods were evaluated and compared with previous methods based on a dataset acquired while driving on 80 km of highway in both daytime and nighttime.

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“…In [20], Hernández et al propose a system based on the reflection of a 2D LiDAR to find the road and road lanes, but it does not produce a road map and was not tested on a real autonomous car. In [21], Kim and Yi also used 2D LiDAR reflectivity to find road lanes and build a lane map. Joshi and James, in [6], combine 3D LiDAR data and OpenStreetMap information to apply a particle filter-based approach, estimating the road lanes centerline and building a road map, but they do not estimate the road lane class.…”

Section: Related Workmentioning

confidence: 99%

Mapping Road Lanes Using Laser Remission and Deep Neural Networks

Carneiro

Nascimento

Guidolini

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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We propose the use of deep neural networks (DNN) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization, in order to allow the operation of autonomous cars in such situations. We take a segmentation approach to the problem and use the Efficient Neural Network (ENet) DNN for segmenting LiDAR remission grid maps into road maps. We represent road maps using what we called road grid maps. Road grid maps are square matrixes and each element of these matrixes represents a small square region of real-world space. The value of each element is a code associated with the semantics of the road map. Our road grid maps contain all information about the roads' lanes required for building the Road Definition Data Files (RDDFs) that are necessary for the operation of our autonomous car, IARA (Intelligent Autonomous Robotic Automobile). We have built a dataset of tens of kilometers of manually marked road lanes and used part of it to train ENet to segment road grid maps from remission grid maps. After being trained, ENet achieved an average segmentation accuracy of 83.7%. We have tested the use of inferred road grid maps in the real world using IARA on a stretch of 3.7 km of urban roads and it has shown performance equivalent to that of the previous IARA's subsystem that uses a manually generated RDDF.

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Lane map building and localization for automated driving using 2D laser rangefinder

Cited by 36 publications

References 13 publications

A Review of Sensor Technologies for Perception in Automated Driving

A Review of Sensor Technologies for Perception in Automated Driving

Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways

Mapping Road Lanes Using Laser Remission and Deep Neural Networks

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