Vision-based rain sensing with an in-vehicle camera

Gormer, Steffen; Kummert, Anton; Park, Su-Birm; Egbert, Peter R.

doi:10.1109/ivs.2009.5164291

Cited by 33 publications

(24 citation statements)

References 4 publications

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“…The profile of friction can be transmitted to the car along with other characteristics of the road. Water height will be measured by roadside units thanks to dedicated devices and spatially refined in real-time with cameras using algorithms such as [25], [26], [41]. Then, using a model of friction estimation depending on water height, real friction can be estimated at any time in the vicinity of the car [42].…”

Section: B Collection Of Infrastructure Parametersmentioning

confidence: 99%

Supporting Drivers in Keeping Safe Speed in Adverse Weather Conditions by Mitigating the Risk Level

Gallen

Hautiére²,

Cord³

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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Abstract-Overspeeding is both a cause and an aggravation factor of traffic accidents. Consequently, lots of efforts are devoted so as to limit overspeeding and consequently to increase the safety of road networks. In this article, a novel approach to compute a safe speed profile to be used in an adaptive Intelligent Speed Adaptation system (ISA) is proposed. The method presents two main novelties. First, the 85 th percentile of observed speeds (V85), estimated along a road section, is used as a reference speed, practiced and practicable in ideal conditions. Second, this reference speed is modulated in adverse weather conditions in order to account for a reduced friction and a reduced visibility distance. The risk is thus mitigated by modulating the potential severity of crashes by means of a generic scenario of accident. Within this scenario, the difference in speed that should be applied in adverse conditions is estimated so that the highway risk is the same as in ideal conditions. The system has been tested on actual data collected on a French secondary road and implemented on a test track and a fleet of vehicles. The performed tests and the experiments of acceptability show a great interest for the deployment of such a system.

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Section: B Collection Of Infrastructure Parametersmentioning

confidence: 99%

Supporting Drivers in Keeping Safe Speed in Adverse Weather Conditions by Mitigating the Risk Level

Gallen

Hautiére²,

Cord³

et al. 2013

IEEE Trans. Intell. Transport. Syst.

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“…This method shows really good results but only when detection area is restraint to sky which is very constraining for a vehicle application. Conversely, [8] uses image processing to detect edges through segmentation of the Sobel mask whilst [9] proposal is to use a special optical device (camera + near infrared light) to obtain a clear and precise image of raindrops on the windscreen.…”

Section: State Of Artmentioning

confidence: 99%

“…So far, methods which have been developed for ITS in the past years [7][8][9] seem to work quite well but they do require either a special optical device [9] or the focus to be set on the windscreen [7], [8]. However, ITS already have many applications using camera sensor (such as road lanes detection or obstacles detection) and both for the sake of their performance as well as for industrial constraints, the camera is usually installed behind the interior rear-view mirror.…”

Section: State Of Artmentioning

confidence: 99%

Detection of unfocused raindrops on a windscreen using low level image processing

Nashashibi

Charrette

Alexandre

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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Abstract-In a scene, rain produces a complex set of visual effects. Obviously, such effects may infer failures in outdoor vision-based systems which could have important side-effects in terms of security applications. For the sake of these applications, rain detection would be useful to adjust their reliability. In this paper, we introduce the problem (almost unprecedented) of unfocused raindrops. Then, we present a first approach to detect these unfocused raindrops on a transparent screen using a spatio-temporal approach to achieve detection in real-time.We successfully tested our algorithm for Intelligent Transport System (ITS) using an on-board camera and thus, detected the raindrops on the windscreen. Our algorithm differs from the others in that we do not need the focus to be set on the windscreen. Therefore, it means that our algorithm may run on the same camera sensor as the other vision-based algorithms.

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“…The profile of friction can be transmitted to the car as other characteristics of the road. Water height will be measured by roadside units and refined in real-time with camera using algorithms such as [20], [21], [22]. Then, using a model of friction estimation depending on water height, real friction can be estimated at a given time in the vicinity of the car.…”

Section: B Infrastructure Parametersmentioning

confidence: 99%

“…In the cooperative framework of an adaptive dynamic ISA, this paper presents the computation onboard in real-time of a safe speed along a path. On one side, contextual information estimated in real-time by in-vehicle sensors are used such as fog detection algorithms [19], rainy situations [20], [21] or wet road detection [22]. On the other side, roadway information is used, road operators could provide data such as reference speed, curvature, slope or superelevation in order to expand the electronic horizon before the car.…”

mentioning

confidence: 99%

Advisory speed for Intelligent Speed Adaptation in adverse conditions

Gallen¹,

Hautiére²,

Glaser³

2010

2010 IEEE Intelligent Vehicles Symposium

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Abstract-In this paper, a novel approach to compute advisory speeds to be used in an adaptive Intelligent Speed Adaptation system (ISA) is proposed. This method is designed to be embedded in the vehicles. It estimates an appropriate speed by fusing in real-time the outputs of ego sensors which detect adverse conditions with roadway characteristics transmitted by distant servers. The method presents two major novelties. First, the 85 th percentile of observed speeds (V85) is estimated along a road, this speed profile is considered as a reference speed practised and practicable in ideal conditions for a lonely vehicle. In adverse conditions, this reference speed is modulated in order to account for lowered friction and lowered visibility distance (top-down approach). Second, this method allows us taking into account the potential seriousness of crashes using a generic scenario of accident. Within this scenario, the difference in speed that should be applied in adverse conditions is estimated so that global injury risk is the same as in ideal conditions.

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Vision-based rain sensing with an in-vehicle camera

Cited by 33 publications

References 4 publications

Supporting Drivers in Keeping Safe Speed in Adverse Weather Conditions by Mitigating the Risk Level

Supporting Drivers in Keeping Safe Speed in Adverse Weather Conditions by Mitigating the Risk Level

Detection of unfocused raindrops on a windscreen using low level image processing

Advisory speed for Intelligent Speed Adaptation in adverse conditions

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