A simulation environment for developing Intelligent Headlight Systems

Tideman, Martijn; Janssen, Simon

doi:10.1109/ivs.2010.5548058

Cited by 11 publications

(5 citation statements)

References 1 publication

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“…ISSN: 2278-0181 http://www.ijert.org light road surfaces are also figured out in [41]. To deal with this issue, more emphasis has been put on the development of the vehicles in [42]. Thus a new discipline of Intelligent Vehicles activities, namely automotive lighting, is coming up [43].…”

Section: International Journal Of Engineering Research and Technology (Ijert)mentioning

confidence: 99%

A Review on Influence of Fog on Road Crash

Singh¹,

Sood²

2017

IJERT

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Adverse weather and road traffic crashes are the foremost causes of motorized vehicle fatalities. The degree of severity may be influenced by a number of factors. Various mechanisms were tested with different conditions and speed response, to understand the reaction of drivers in changing fog levels. New algorithms have been proposed in recent studies for rating visibility enhancement techniques, on the basis of summation of various types of fog generated on artificial and camera images. The main concern should be the development of effective detection of fog and warning system, with respect to speed and headway. Further research analysis is necessary to quantify the influences of deicing materials, particularly in relation with frequency and application rate, on transport safety. A state-of-the-art critique of study on relationship amidst fog and highway safety is provided in this paper. Reduction in the count of fatalities is expected in future on account of development of Advanced Driving Assistance Systems (ADAS).

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Section: International Journal Of Engineering Research and Technology (Ijert)mentioning

confidence: 99%

A Review on Influence of Fog on Road Crash

Singh¹,

Sood²

2017

IJERT

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“…Among the factors that explain these figures, drowsiness [2], speed [3], and reduced visibility [4] are frequently cited. To address this issue, emphasis is put on the development of smarter lighting strategies for vehicles [5]. Advanced systems are being developed to automate and adjust the operation of vehicle lights to maximize visibility while preventing glare for oncoming drivers.…”

Section: Introductionmentioning

confidence: 99%

Nighttime Visibility Analysis and Estimation Method in the Presence of Dense Fog

Gallen

Cord²,

Hautiére³

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Compared with daytime, a larger proportion of road accidents happens during nighttime. The altered visibility for drivers partially explains this situation. It becomes worse when dense fog is present. In this paper, we first define a standard night visibility index, which allows specifying the type of fog that an advanced driver assistance system should recognize. A methodology to detect the presence of night fog and characterize its density in images grabbed by an in-vehicle camera is then proposed. The detection method relies on the visual effects of night fog. A first approach evaluates the presence of fog around a vehicle due to the detection of the backscattered veil created by the headlamps. In this aim, a correlation index is computed between the current image and a reference image where the fog density is known. It works when the vehicle is alone on a highway without external light sources. A second approach evaluates the presence of fog due to the detection of halos around light sources ahead of the vehicle. It works with oncoming traffic and public lighting. Both approaches are illustrated with actual images of fog. Their complementarity makes it possible to envision a complete night-fog detection system. If fog is detected, its characterization is achieved by fitting the different correlation indexes with an empirical model. Experimental results show the efficiency of the proposed method. The main applications for such a system are, for instance, automation or adaptation of vehicle lights, contextual speed computation, and reliability improvement for camera-based systems.

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“…Transportation systems pose numerous problems such as traffic accidents, traffic congestion,high energy consumption, and air pollution.One of the ways to overcome these problems is to develop automated vehicles [1][2][3][4][5][6][7][8][9]. Auto mated vehicles can contribute to the improvement of transportation through their h igh capacity, increased safety, low emission and high efficiency.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study under Real-World Conditions to Develop Fault Detection for Automated Vehicles

Hashimoto¹,

Özgüner²,

Yokozuka³

et al. 2012

JMEA

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Automated vehicles can contribute to the improvement of transportation through their high capacity, increased safety, low emission and high efficiency. However, unstable conditions of automated mobile systems, which include automated vehicles and mobile robots) can cause serious problems, andthus, automated mobile system requiresto be highly reliable. The objective of this research is to develop on analgorith mfor detection faults (unstable condition) in an automated mobile system and to improve the overall reliability of this system. In this study, we in itially stored and updated a few patterns of data constellations under normal and unstable conditions for fault identification through real-world experiments. Multiple experiments were performed in a public urban area (with course distance per set beingapproximately1.1[km]), where several pedestrians, bicycles, and other robots were also present. The method used for detecting faults utilizes Mahalanobis distance, correlat ion coefficient, and linearization in order to enhance the accuracy of detecting faults;further, because real-world experimental conditions vary frequently,it is essential for the proposed method to be robust undervarious conditions. The ma in feature of this study is that it involves the use of experimental results obtained under real-world conditions, to develop a fault detection algorithm and evaluate its validity. In addition, simu lations were performed using the real-world experimental data, wh ich includes newly logged experimental data after the algorithm was developed in order to evaluate the validity of the proposed algorithm. The simulat ion results show that the proposed algorithm detects faults accurately, thus, they prove its validity.

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A simulation environment for developing Intelligent Headlight Systems

Cited by 11 publications

References 1 publication

A Review on Influence of Fog on Road Crash

A Review on Influence of Fog on Road Crash

Nighttime Visibility Analysis and Estimation Method in the Presence of Dense Fog

Experimental Study under Real-World Conditions to Develop Fault Detection for Automated Vehicles

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