A lane marker estimation method for improving lane detection

Revilloud, Marc; Gruyer, Dominique; Rahal, Mohamed-Cherif

doi:10.1109/itsc.2016.7795569

Cited by 12 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the results on MM would not be relevant. As discussed in [48] [13], in most of the work presented in the literature, the aim is to find the white strips in the image without seeking for consecutive elements of lane marking. Cnversely, in our work, we are more interested in the curvature of the lane marking than the white strips.…”

Section: Real-world Experimental Resultsmentioning

confidence: 99%

End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

Locating the vehicle in its road is a critical part of any autonomous vehicle system and has been subject to different research topics. In most works presented in the literature, ego-localization is split into three parts: Road level-localization consisting in the road on which the vehicle travels, Lane level localization which is the lane on which the vehicle travels, and Ego lane level localization being the lateral position of the vehicle in the ego-lane. For each part, several researches have been conducted. However, the relationship between the different parts has not been taken into consideration. Through this work, an end-to-end ego-localization framework is introduced with two main novelties. The first one is the proposition of a complete solution that tackles every part of the ego-localization. The second one lies in the information-driven approach used. Indeed, we use prior about the road structure from a digital map in order to reduce the space complexity for the recognition process. Besides, several fusion framework techniques based on Bayesian Network and Hidden Markov Model are elaborated leading to an ego-localization method that is, to a large extent, robust to erroneous sensor data. The robustness of the proposed method is proven on different datasets in varying scenarios.

show abstract

Section: Real-world Experimental Resultsmentioning

confidence: 99%

End-to-End Probabilistic Ego-Vehicle Localization Framework

Kasmi

Laconte

Aufrère

et al. 2021

IEEE Trans. Intell. Veh.

View full text Add to dashboard Cite

show abstract

“…Common image enhancement methods can be divided into spatial domain enhancement and frequency domain enhancement. In this paper, we compared various filter methods [15, 39–41] (mean filter 4 × 4, Gaussian filter 4 × 4, median filter 4 × 4, ideal low‐pass filter) on the same image. Then the local lane part of the road image could be partially enlarged, and the effect of the filtering method on the edge of the lane line is compared.…”

Section: Lane Detectionmentioning

confidence: 99%

Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

Xiong

Liu

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

“…An autonomous vehicle employs several different sensors to analyse the road on which it travels. VEDECOM chooses a multi-agent approach based on a camera [1]. A reliable marking line is more than 95% detected with fewer than 2% of false alarms [1].…”

Section: Introductionmentioning

confidence: 99%

“…VEDECOM chooses a multi-agent approach based on a camera [1]. A reliable marking line is more than 95% detected with fewer than 2% of false alarms [1]. Applied on a degraded road infrastructure, 85% of the emergency line is correctly detected and the center line is not seen by the system [2].…”

Section: Introductionmentioning

confidence: 99%

A Clustering-based Approach to Segment a Pavement Markings Line

Redondin¹,

Bouillaut²,

Daucher³

2020

Int J Performability Eng

View full text Add to dashboard Cite

The maintenance of road infrastructure is a classic social challenge, especially in the context of a decreasing maintenance budget and the advent of autonomous vehicle traffic. Road markings need an accurate replacement strategy to guarantee that the markings remain perceptible. The retroreflective luminance of markings is currently dynamically quantifiable only by using a retroreflectometer such as the Ecodyn from MLPC. The main objective of this research is to construct a performance-based approach for retroreflective marking replacement adapted to a given road network. This approach involves three main tasks: localize the strategic area based on past inspections, determine an adapted decay model for a given area, and evaluate the economic impact of replacing markings. This paper focuses on the first task. We apply the Agglomerative Hierarchical Clustering (AHC) method to a given dataset to obtain a suitable markings line segmentation. Markings whose retroreflective luminance exhibits similar evolution over time are interpreted to belong to a specific area of the road network. When no follow-up replacement has occurred, a replacement detector deduces the date at which markings were laid from the clusters. The broken center line of the French National Road 4 illustrates the proposed approach; the road is divided into 5 clusters and 34 lifecycles. A study of markings laid in 2008 and replaced in 2012 shows important variations in the decay of the retroreflective luminance as identified by the clustering approach. Even for a single road, an optimal replacement strategy for retroreflective road markings is necessary and is composed of several local maintenance strategies.

show abstract

A lane marker estimation method for improving lane detection

Cited by 12 publications

References 10 publications

End-to-End Probabilistic Ego-Vehicle Localization Framework

End-to-End Probabilistic Ego-Vehicle Localization Framework

Fast and robust approaches for lane detection using multi‐camera fusion in complex scenes

A Clustering-based Approach to Segment a Pavement Markings Line

Contact Info

Product

Resources

About