Road boundary detection for run-off road prevention based on the fusion of video and radar

Janda, Florian; Pangerl, Sebastian; Lang, Eva; Fuchs, Ernst

doi:10.1109/ivs.2013.6629625

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…Road limits have been estimated with a maximum error of half a lane at zero distance from the host vehicle and less than one lane width at 50 meters distance. This information can be fused with camera images to improve both detections [74][75][76].…”

Section: ) Radar 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.

215

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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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Section: ) Radar 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.

215

View full text Add to dashboard Cite

show abstract

“…In [12], they attempted to solve the problem through guardrail modeling and target tracking. Furthermore, in [13] and [14], they attempted to improve the target detection performance through bridge and road boundary detection, respectively. However, these studies [12]- [14] have a disadvantage, that is, they can be applied only to a specific road environment.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, in [13] and [14], they attempted to improve the target detection performance through bridge and road boundary detection, respectively. However, these studies [12]- [14] have a disadvantage, that is, they can be applied only to a specific road environment. In [15], they proposed a technique for recognizing iron tunnels using entropy of received signal amplitude.…”

Section: Introductionmentioning

confidence: 99%

Road Environment Recognition for Automotive FMCW RADAR Systems Through Convolutional Neural Network

Sim

Lee

et al. 2020

IEEE Access

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In this study, we propose a method to recognize road environments with automotive frequency-modulated continuous wave (FMCW) radar systems. For automotive radar systems on the road, diverse road environments are observed. Each road environment generates unnecessary echoes known as clutter, and the magnitude distribution of received radar signal varies depending on road structures. Therefore, it is necessary to classify the road environment and adopt a target detection algorithm suitable for each road environment characteristic. To recognize the road environment in advance, it is necessary to identify the section where the road environment changes. In this paper, we define a changed road area as a transition region, and we classify the road environment and transition regions to improve the road environment recognition performance. Road environments are recognized by applying convolutional neural networks to the frequency-domain received signals of 77 GHz FMCW automotive radar systems. Experimental results in real-road environments demonstrate that the proposed method achieves 100% recognition performance, which is better if compared with that of the conventional methods. INDEX TERMS Automotive frequency-modulated continuous wave (FMCW) radar, road environment recognition, convolutional neural network.

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“…Some research has been conducted concerning clutter in road environments [3, 4]. Above all, only a few studies have been conducted on radar clutter in road environments, where metallic structures with intense reflections are densely distributed [5, 6]. When a radar‐equipped vehicle travels through specific road structures made of iron such as tunnels, soundproof walls, and guardrails, the radar detection performance can be degraded [7–9].…”

Section: Introductionmentioning

confidence: 99%