Hough transform based ego‐velocity estimation in automotive radar system

Electronics LettersVolume 57, Issue 2 p. 80-82 ORIGINAL RESEARCH PAPEROpen Access Hough transform based ego-velocity estimation in automotive radar system Sohee Lim, Seoul National University, Seoul, Republic of KoreaSearch for more papers by this authorSeongwook Lee, Corresponding Author swl90@kau.ac.kr orcid.org/0000-0001-9115-4897 Korea Aerospace University, Gyeonggi-do, Republic of Korea Email: swl90@kau.ac.krSearch for more papers by this author Sohee Lim, Seoul National University, Seoul, Republic of KoreaSearch for more papers by this authorSeongwook Lee, Corresponding Author swl90@kau.ac.kr orcid.org/0000-0001-9115-4897 Korea Aerospace University, Gyeonggi-do, Republic of Korea Email: swl90@kau.ac.krSearch for more papers by this author First published: 31 December 2020 https://doi.org/10.1049/ell2.12048Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract In general, the velocity estimated through the automotive radar is the relative velocity between the ego-vehicle and the target. To estimate the absolute velocity of the ego-vehicle (i.e. ego-velocity), it is necessary to get help from other automotive sensors, such as cameras and lidars. In this letter, therefore, a method for estimating the ego-velocity is proposed using only radar sensor data. To estimate the ego-velocity, stationary targets in the detection result have to be identified because the velocity relative to the stationary target can be directly converted to the ego-velocity. In the proposed method, the radar data is first processed to extract the relative distance from the ego-vehicle to the target, relative velocity between the ego-vehicle and the target, and angle between the ego-vehicle and the target. Then, the detected targets are represented in the angle-velocity domain because moving and stationary targets in this domain exhibit different characteristics. In other words, the moving targets are randomly placed, but the stationary targets exist around a specific line. Finally, the Hough transformation is applied to automatically determine the line, and the ego-velocity is calculated from the transform result. The proposed method shows a maximum error of 2.4% in the velocity range of 30–90 km/h. Introduction To realize autonomous driving, data obtained from automotive sensors such as cameras, lidars, and radars must be used together. The main purpose of using these sensors is to effectively detect targets located around the ego-vehicle. For accurate target detection, it is important to identify the absolute movement of the ego-vehicle relative to the surrounding environments, which is called ego-motion estimation. For example, the authors in [1] estimated the ego-motion using deep neural networks in a single monocular camera system. In addition, in [2], laser range data is used to estimate the ego-motion of the vehicle in real time. To obtain complete information on ego-motion, the position and velocity of the ego-vehicle must be identified. In general, using radar sensor data, it is possible to estimate the relative distance to the target and the relative velocity of the target. When considering the position of the ego-vehicle as the origin, the relative distance can be directly converted into the absolute distance. However, to convert the relative velocity into the absolute velocity, we need to know the velocity of the ego-vehicle. Therefore, this letter proposes a method for estimating the velocity of the ego-vehicle using only radar sensor data without the help of other vehicle sensors. The most important thing in estimating the velocity of the ego-vehicle is to identify stationary targets among the detected targets because they can be used as reference targets. In other words, the velocity relative to the stationary target can be directly converted to the velocity of the ego-vehicle. In this work, we use the detection result in the angle-velocity domain to discriminate the stationary targets. In this domain, the moving and the stationary targets exhibit different patterns. In other words, the detection points corresponding to stationary targets exist on a specific curve, but the points corresponding to moving targets are randomly scattered on a two-dimensional plane. Therefore, if the curve formulated by points corresponding to the stationary targets is estimated, we can calculate the absolute velocity of the ego-vehicle. When several points are distributed in a plane, methods such as the linear least squares (LLS) [3] and random sampling consensus (RANSAC) [4], [5] are widely used to find the tendency of these points. In this work, we use the Hough transform [6] to extract only points corresponding to the stationary targets in the angle-velocity domain. This transformation can be effectively used to find inliers when points are scattered on the two-dimensional plane. Some studies have been conducted using Hough transforms on radar sensor data [7], [8]. For example, a vehicle moving at a constant velocity in the time-distance domain was extracted through the Hough transformation in [7]. In addition, patterns of moving and stationary targets were classified by applying the Hough transform to the time-frequency spectrum in the automotive radar system [8]. When this transformation is applied to detection points located in the angle-velocity domain, we can find the trend line corresponding to the stationary targets. Then, from the estimated trend line, the velocity of the ego-vehicle is calculated directly. To evaluate the estimation performance of the proposed method, radar sensor data obtained from actual road environments are used. In our experiment, a frequency-modulated continuous wave (FMCW) radar is mounted on the vehicle. The remainder of this letter is organized as follows: First, we introduce the automotive radar system used in the experiment. Then, the target detection result in a actual road environment is described. Next, we explain the Hough transform and how to apply it to estimate the absolute velocity of the ego-vehicle. Finally, we conclude this letter. Automotive radar system used in experiments The automotive radar we used in this study consists of the transmit antenna (Tx), receiving antenna array (Rx), waveform generator (WG), voltage-controlled oscillator (VCO), frequency mixer, low-pass filter (LPF), analog-to-digital converter (ADC), and digital signal processor (DSP), as shown in Figure 1. The WG generates an FMCW with a center frequency of , a bandwidth of , and a sweep time of . As shown in Figure 1, the waveform is composed of an up-chirp and a down-chirp whose frequency linearly increase and decreases with time, respectively. By extracting beat frequencies from each chirp, we can estimate the relative distance to the target and the relative velocity of the target [9]. In addition, the antenna array is used to estimate the angle of the target, which consists of 4 antenna elements. The distance between the antenna elements is , where denotes the speed of light. In our radar system, the Bartlett algorithm [10] is applied to the signals received at Rx to estimate the angle of the target. Fig. 1Open in figure viewerPowerPoint Configuration of the 77 GHz automotive radar system Target detection result through radar signal processing We mounted the automotive radar sensor described in the previous section on the bumper of the vehicle and conducted experiments on the road. In our measurements, we set the values of , , and to 76.5 GHz, 500 MHz, and 5 ms, respectively. Figure 2a shows one of the road environments where we acquired radar sensor data. In this case, the velocity of the vehicle was maintained at 65 km/h. By processing the radar signal acquired in this environment, we can estimate the relative distance , the relative velocity in the radial direction, and the angle information of the target, where is the index of each detected target. Then, using the target information represented by the polar coordinate system (i.e. ), the positions of the targets can be converted into a two-dimensional Cartesian coordinate system (i.e. , as shown in Figure 2b. The target detection result corresponding to Figure 2a is shown in Figure 3. In this figure, including a target vehicle located in front, road structures such as guardrails around the ego-vehicle are also detected by the radar sensor. Fig. 2Open in figure viewerPowerPoint Target detection using the automotive radar: (a) radar signal measurement on the bridge (b) coordinate system conversion of the radar detection result Fig. 3Open in figure viewerPowerPoint Target detection result on the bridge Stationary and moving targets in the angle-velocity domain To estimate the ego-velocity, it is important to select only fixed targets among the detected targets. This is because the relative velocity to a stationary target means the absolute velocity of the ego-vehicle. Referring to Figure 2b, the velocity of the target vehicle in the -axis can be expressed as . We redraw the detection result in Figure 3 in the angle-velocity (i.e. and ) domain, as shown in Figure 4. In this domain, moving and stationary targets exhibit different characteristics. The moving targets are randomly placed, but the stationary targets exist around a specific line. Therefore, if the line formed by the stationary targets is estimated, we can discriminate the stationary targets, and it is possible to estimate the absolute velocity of the ego-vehicle. Fig. 4Open in figure viewerPowerPoint Target detection result in the angle-velocity domain Hough transform based ego-velocity estimation In this work, we use the Hough transform to find the tendency from the scattered points in the angle-velocity domain. The Hough transform is widely used to extract linear components from the image. For example, as shown in Figure 5a, if there are two points in the image space, a straight line with the two points can be expressed in the slope-intercept form as , where is the slope and is the -intercept of the straight line. This line can also be expressed as a single point in the parameter space, as shown in Figure 5b. In the parameter space, however, a vertical line parallel to the -axis cannot be represented. Thus, in the Hough transform, this straight line is represented using the distance from the origin and the angle between the -axis and , as shown in Figure 5c, which means each straight line in the - domain can be expressed as a point in the - domain. The straight lines passing through each point in Figure 5a is expressed as points on each curve in Figure 5d. Finally, the red point in Figure 5d indicates the point corresponding to a straight line passing through two points. Thus, from the point of intersection, we can estimate the straight line formed by the points through the Hough transform. Fig. 5Open in figure viewerPowerPoint Basic concept of the Hough transform: (a) in the – domain; (b) in the - domain; (c) in the - domain; (d) in the - domain Figure 6 shows the result of applying the Hough transform to the detection result in the angle-velocity domain of Figure 4. In the Hough transform domain, several curves meet at one point . Thus, the straight line corresponding to is the most obvious straight line, which can be expressed as (i.e. ) in the original image space. We plot the estimated line back on the detection result in the angle-velocity domain, as shown in Figure 7. Because the slope of this line is close to zero, the ego-velocity can be estimated using the -intercept. Thus, the absolute velocity of the ego-vehicle is estimated as 63.7 km/h, which is very close to the actual velocity of 65 km/h. Considering the concept of relative velocity, the absolute value of the -intercept becomes the ego-velocity. Fig. 6Open in figure viewerPowerPoint Result of applying the Hough transform to the detection result in the angle-velocity domain Fig. 7Open in figure viewerPowerPoint Ego-velocity estimation results in the angle-velocity domain We also estimated the absolute velocity of the ego-vehicle by applying the LLS method and the RANSAC algorithm proposed in [11] to the detection result in angle-velocity domain, as shown in Figure 7. In the case of the LLS, the estimation result is not accurate compared to other methods because this method calculates a trend line using both detection results corresponding to stationary and moving targets. The RANSAC algorithm finds the trend line quite accurately based on inliers. However, in RANSAC-based methods, parameter values, such as the minimum sample size and maximum distance, must be set empirically, which is not required in the Hough transform. Finally, we verified the performance of our proposed method by changing the velocity of the ego-vehicle, as shown in Table 1. The estimation accuracy decreased slightly as the velocity increased, but the maximum error was 2.4% of the actual velocity. Through our proposed method, it is possible to accurately estimate the ego-velocity by automatically selecting points corresponding to stationary targets among several detection points. Table 1. Ego-velocity estimation results according to the velocity of the ego-vehicle Actual velocity (km/h) 35.0 45.0 55.0 65.0 75.0 85.0 Estimated velocity (km/h) 34.7 44.3 54.0 63.7 73.5 83.0 Conclusion In this letter, we proposed a method for estimating the absolute velocity of the ego-vehicle using only the radar detection result. To estimate the velocity of the ego-vehicle, it is important to identify stationary targets from the radar detection results. Thus, we used the distribution of targets in the angle-velocity domain. In this domain, stationary targets existed on a specific line, and the Hough transformation was used to estimate the line. We verified the performance of the proposed method with the radar data measured on the actual road. Through the Hough transform, inliers corresponding to the stationary targets were effectively extracted among all the detection points. The proposed method has a maximum error of 2.4% in the velocity range of 30–90 km/h. Acknowledgement This work was supported by the 2020 Korea Aerospace University Faculty Research Grant and also by the Technology Innovation Program (or Industrial Strategic Technology Development Program, No. 10080086, Development of 77/79 GHz Dual Band Radar) funded By the Ministry of Trade, Industry and Energy (MOTIE, Korea). 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