In this paper, we proposed a human-vehicle classification scheme using a Doppler spectrum distribution based on 2D Range-Doppler FMCW (Frequency Modulated Continuous Wave). Typically, because humans have non-rigid motion, multiple reflection points can appear on the Doppler spectrum. However, in th e actual field, the Doppler spectrum distribution of a walking human is highly variable over time. Thus method using only this characteristic of the extended Doppler spectrum is limited with regard to human-vehicle classification. In order to improve the target classification performance, we designed two feature. The first is the Doppler spectrum extension features, which is expressed as the number of Doppler reflection points with magnitudes exceeding reference threshold. Next, we defined the Doppler spectrum variance feature, which is extracted as the difference the reflection points between two successive frames. We can determine how the Doppler spectrum expands with the first feature, and how the Doppler spectra change based on the second feature. To verify the proposed target classification scheme, we measured real data using a 24 GHz FMCW transceiver on an actual road with various scenarios of walking humans and moving vehicles. From an analysis of the results, we confirmed that the thresholds effectively classify humans and vehicles based on the two proposed features. Finally, we verified that the results of the proposed classification scheme using the two features were much better than those using the first feature alone.
In this paper, we propose an efficient buffer management scheme to increase performance of the PCI express architecture. It is necessary for the several buffers such as replay buffer in virtual channel and traffic class buffers to implement the PCI express architecture. The proposed scheme merges the buffers into only one buffer and dynamically adjusts size of the replay buffer space and traffic class buffers space to effectively support the ordering requirements of the PCI express. The simulation result shows 30% performance improvement over the conventional scheme that uses separated buffers.
PCI 2.2 마스터 디바이스가 타겟 디바이스로부터 데이터를 읽어 오고자 할 때 타겟 디바이스는 내부적으로 데이터를 준비해야 함으로 인해 PCI 버스가 데이터 전송 없이 점유되는 상황이 발생한다. 이를 위해 PCI 2.2 사양에서는 지연전송을 제안하여 전송 효율을 향상시켰지만 이 역시 타겟 디바이스가 얼마의 데이터를 미리 준비 해둘지를 알 수 없어 버스 사용 및 데이터 전송 효율을 떨어뜨리는 원인을 제공한다. 이에 앞선 연구에서는 이를 해결하기 위한 프리페치 요구를 이용하는 새로운 방법을 제안하였다. 본 논문에서는 이 방법을 지원하는 PCI 타겟 컨트롤러와 로컬 디바이스를 설계하였다. 설계된 PCI 타겟 컨트롤러는 간단한 로컬 인터페이스를 가질 뿐 아니라 PCI 2.2를 전혀 모르는 사용자도 쉽게 PCI 인터페이스를 지원할 수 있도록 설계되었다. 또한 설계된 하드웨어를 효과적으로 검증하기 위한 방법으로 기본 동작 검증, 설계 기반검증, 그리고 랜덤 테스트 검증을 제안하였다 이러한 검증을 위해 테스트 벤치와 테스트 벤치를 동작시키는 위한 명령어를 제안하였다. 그리고 랜덤 테스트를 위해 참조 모델, 랜덤 발생기, 비교 엔진으로 구성된 테스트 환경을 구축하였으며 이를 이용해 코너 케이스를 효과적으로 검증할 수 있다. 또한 제안된 테스트 환경을 통해 시뮬레이션 한 결과, 프리페치 요구를 이용한 제안된 방법이 지연 전송에 비해 데이터 전송 효율이 평균 $9\%$ 향상되었다. When a PCI 2.2 bus master requests data using Memory Read command, a target device may hold PCI bus without data to be transferred for long time because a target device needs time to prepare data infernally. Because the usage efficiency of the PCI bus and the data transfer efficiency are decreased due to this situation, the PCI specification recommends to use the Delayed Transaction mechanism to improve the system performance. But the mechanism cann't fully improve performance because a target device doesn't know the exact size of prefetched data. In the previous work, we propose a new method called Prefetch Request when a bus master intends to read data from the target device. In this paper, we design PCI 2.2 controller and local device that support the proposed method. The designed PCI 2.2 controller has simple local interface and it is used to convert the PCI protocol into the local protocol. So the typical users, who don't know the PCI protocol, can easily design the PCI target device using the proposed PCI controller. We propose the basic behavioral verification, hardware design verification, and random test verification to verify the designed hardware. We also build the test bench and define assembler instructions. And we propose random testing environment, which consist of reference model, random generator ,and compare engine, to efficiently verify corner case. This verification environment is excellent to find error which is not detected by general test vector. Also, the simulation under the proposed test environment shows that the proposed method has the higher data transfer efficiency than the Delayed Transaction about $9\%$.
Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar–radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.
For an automotive pedestrian detection radar system, fast-ramp based 2D range-Doppler Frequency Modulated Continuous Wave (FMCW) radar is effective for distinguishing between moving targets and unwanted clutter. However, when a weak moving target such as a pedestrian exists together with strong clutter, the pedestrian may be masked by the side-lobe of the clutter even though they are notably separated in the Doppler dimension. To prevent this problem, one popular solution is the use of a windowing scheme with a weighting function. However, this method leads to a spread spectrum, so the pedestrian with weak signal power and slow Doppler may also be masked by the main-lobe of clutter. With a fast-ramp based FMCW radar, if the target is moving, the complex spectrum of the range- Fast Fourier Transform (FFT) is changed with a constant phase difference over ramps. In contrast, the clutter exhibits constant phase irrespective of the ramps. Based on this fact, in this paper we propose a pedestrian detection for highly cluttered environments using a coherent phase difference method. By detecting the coherent phase difference from the complex spectrum of the range-FFT, we first extract the range profile of the moving pedestrians. Then, through the Doppler FFT, we obtain the 2D range-Doppler map for only the pedestrian. To test the proposed detection scheme, we have developed a real-time data logging system with a 24 GHz FMCW transceiver. In laboratory tests, we verified that the signal processing results from the proposed method were much better than those expected from the conventional 2D FFT-based detection method.
Today, 77GHz FMCW (Frequency Modulation Continuous Wave) radar sensors are used for automotive applications. In typical automotive radar, the target of interest is a moving target. Thus, to improve the detection probability and reduce the false alarm rate, an MTD(Moving Target Detection) algorithm should be required. This paper describes the proposed two-step MTD algorithm. The 1st MTD processing consists of a clutter cancellation step and a noise cancellation step. The two steps can cancel almost all clutter including stationary targets. However, clutter still remains among the interest beat frequencies detected during the 1st MTD and CFAR (Constant False Alarm) processing. Thus, in the 2nd MTD step, we remove the rest of the clutter with zero phase variation.
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