Pedestrian classification in automotive radar systems

Heuel, Steffen; Rohling, Hermann

doi:10.1109/irs.2012.6233285

Cited by 90 publications

(69 citation statements)

References 5 publications

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“…The decision for either of the two classes is then reduced to comparing the value of y being greater or smaller then 0.5. In contrast to other approaches, namely in [8], the classification is done indepently for every single measurement without feedback from previous classification results from the tracker. By this, the classification performance is constant across all observations, still, the classification result might fluctuate along a track.…”

Section: Classification Processmentioning

confidence: 99%

Pedestrian classification with 24 GHz chirp sequence radar

Sorowka¹,

Rohling²

2015

2015 16th International Radar Symposium (IRS)

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Driver assistance systems based on radar are available today in series production in consumer vehicles. The radar sensor based automotive applications already help to reduce road accidents but is usually limited to highway scenarios. However, there are too many fatalities in public roads. Most of them involve pedestrians and happen in urban road situations. For assisting drivers in those cases, a novel 24 GHz Radar Sensor has been developed within the EU-funded project ARTRAC. The main focus of this paper is to present the pedestrian classification scheme that is incorporated within this sensor. Additionally, the derived requirements and the role of the classification process within the cognitive collision mitigation system is pointed out.

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Section: Classification Processmentioning

confidence: 99%

Pedestrian classification with 24 GHz chirp sequence radar

Sorowka¹,

Rohling²

2015

2015 16th International Radar Symposium (IRS)

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“…Due to the frequency homogeneity of the point backscatters from this type of target, it is expected that it will present a narrow Doppler spectrum [7,8]. Next, we consider a pedestrian target, with a displacement velocity (torso velocity) of 2m/s.…”

Section: Environment Descriptionmentioning

confidence: 99%

“…Next, we consider a pedestrian target, with a displacement velocity (torso velocity) of 2m/s. Due to rotations and translations of legs and arms superimposed to the displacement movement, the spectrum of the pedestrian presents velocities ranging from zero to approximately 4 times higher than the torso mean velocity, which yields an expanded micro Doppler spectrum [8]. For our simulations we will use a typical model for description of pedestrian movement based on the six-point model depicted on Figure 2.…”

Section: Environment Descriptionmentioning

confidence: 99%

Comparative of signal processing techniques for micro-Doppler signature extraction with automotive radar systems

2014

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In recent years, the automotive industry has experienced an evolution toward more powerful driver assistance systems that provide enhanced vehicle safety. These systems typically operate in the optical and microwave regions of the electromagnetic spectrum and have demonstrated high efficiency in collision and risk avoidance. Microwave radar systems are particularly relevant due to their operational robustness under adverse weather or illumination conditions. Our objective is to study different signal processing techniques suitable for extraction of accurate micro-Doppler signatures of slow moving objects in dense urban environments. Selection of the appropriate signal processing technique is crucial for the extraction of accurate micro-Doppler signatures that will lead to better results in a radar classifier system. For this purpose, we perform simulations of typical radar detection responses in common driving situations and conduct the analysis with several signal processing algorithms, including short time Fourier Transform, continuous wavelet or Kernel based analysis methods. We take into account factors such as the relative movement between the host vehicle and the target, and the non-stationary nature of the target's movement. A comparison of results reveals that short time Fourier Transform would be the best approach for detection and tracking purposes, while the continuous wavelet would be the best suited for classification purposes.

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“…Feature Annotation Description x 9 R profile,buf Extension in range x 10 std( R,buf) Standard deviation in range x 11 var( R,buf) Variance in range x 12 v r,buf Radial Velocity x 13 v profile,buf Extension in velocity x 14 std(v r,buf ) Standard deviation in velocity x 15 var(v r,buf ) Variance in velocity x 16 scatterer,buf Number of scatterers Table 4. An additional feature set extracted from multiple measurements.…”

Section: Feature Extractionmentioning

confidence: 99%