Radar-interference-based bridge identification for collision avoidance systems

Diewald, Fabian; Klappstein, Jens; Sarholz, Frederik; Dickmann, Juergen; Dietmayer, Klaus

doi:10.1109/ivs.2011.5940422

Cited by 46 publications

(31 citation statements)

References 2 publications

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“…These methods cannot be applied to our case as the lidar configuration suggested may lead to occlusion of vehicles. Diewald et al [10] use the Parzen window and then Gaussian kernels to detect the bridges in the scene whereas we want to detect objects at variable and fast speeds (PTWs). Li et al [11] propose a tensor-based method for a general purpose feature detector but this method constructs a scene of the static environment which is not our case study.…”

Section: Research Work Done To Datementioning

confidence: 99%

A lidar-based method for the detection and counting of Powered Two Wheelers

Prabhakar¹,

Subirats²,

Lecomte³

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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The safety of Powered Two Wheelers (PTWs) is important for public authorities and road administrators around the world. Official figures show that PTW represent only 2% of the total traffic on French roads, but as these figures are obtained by simply counting the number plates registered, they do not give a true picture of the PTWs on the road at any given moment. To date, there is no overall solution to this problem that uses a sensor capable of detecting PTWs and taking into consideration their interaction with the other vehicles on the road (for example: I nter-lane traffic, when PTWs move in between two lanes on a highway), and no stateof-the-art technical solutions can be adapted to measure this category of vehicle in traffic (unlike cars and trucks). The research work in this domain has therefore, not been greatly developed which is an issue of concern. I n this paper we present a new method of detecting PTWs by using a single-plane lidar, named the Last Line Check (LLC) method. This method uses the energy of the last scan to extract the information. After extraction, a Support Vector M achine (SVM ) is used for classification. The LLC method is tested in real time and gives interesting results with a high precision.

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Section: Research Work Done To Datementioning

confidence: 99%

A lidar-based method for the detection and counting of Powered Two Wheelers

Prabhakar¹,

Subirats²,

Lecomte³

et al. 2013

2013 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…In addition, there have been several approaches to discriminate over-head or neighboring structures from stationary targets on roads. [15][16][17] A recent study has introduced a recognition method for ITs that influence the detection performance of radars because of large reflections. 18 However, few of them considered the suppression method to improve the detection performance despite clutters on roads.…”

Section: Introductionmentioning

confidence: 99%

Harmonic clutter recognition and suppression for automotive radar sensors

Lee

Lim

Jeong

et al. 2017

International Journal of Distributed Sensor Networks

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In this article, we propose a novel harmonic clutter recognition and suppression method to overcome the deterioration of a target-or vehicle-detection performance due to harmonic clutters. Although several studies have been performed on the reflection and diffraction on road surfaces for automotive radar sensors, most of them did not consider the case where metallic structures such as iron tunnels with greater reflection are densely distributed. The proposed method measures the periodicity of harmonic clutters by analyzing the spectral characteristics of the received radar signal with various road conditions. The proposed method can successfully recognize harmonic clutters. In addition, experimental results show that early detection of a target vehicle in an iron tunnel under adaptive cruise control is improved using the proposed clutter suppression method.

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“…For automotive applications, a bridge identification algorithm [3] based on a multipath interference pattern was proposed. The applied pattern consisted of variation in the back-scattered power from the phase differences of the direct path to the target and the indirect path, while driving towards bridges or stationary obstacles.…”

Section: Introductionmentioning

confidence: 99%

A new height-estimation method using FMCW radar Doppler beam sharpening

Laribi

Hahn

Dickmann

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents a new method for estimating the height of extended objects using a Frequency Modulation Continuous Wave (FMCW) automotive radar. The proposed algorithm exploits the frequency shift caused by the Doppler effect while approaching stationary objects, to estimate target heights. Thus, the algorithm does not require multiple vertical antennas for height finding. First, the measured radial velocity is derived using sensor target geometry, then, a target height is formulated as a function of target range, vehicle velocity and elevation angle component of the measured radial velocity. Next, the processing pipeline of the proposed Doppler Beam Sharpening (DBS) algorithm is described, and the three dimensional (3D) high resolution RELAX is applied to collected radar data to provide accurate range, azimuth angle and Doppler estimations of the detected targets. Finally height measurement results of an entrance gate 4.5 m high are presented and discussed. The results show that the proposed height finding algorithm can achieve a root mean squared error of 0.26 m.

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Radar-interference-based bridge identification for collision avoidance systems

Cited by 46 publications

References 2 publications

A lidar-based method for the detection and counting of Powered Two Wheelers

A lidar-based method for the detection and counting of Powered Two Wheelers

Harmonic clutter recognition and suppression for automotive radar sensors

A new height-estimation method using FMCW radar Doppler beam sharpening

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