Figure 4
Contexts in source publication
Context 1
... model and visualize the environment around the vehicle in any places, the grid map coordinate system needs to move with the ego vehicle like in method 2. Meanwhile, some modifications are applied to solve the offset problem. According to the vehicle position, the grid map is just shifted with integer rows and columns in x-and y-direction. The rest difference between the origin point of the grid map and the ego position and is retained (see Figure 4). The orientation of the grid map is fixed by using the ego vehicle direction from the first measurement. During the vehicle motion the grid map is not rotated, instead the orientation of the ego vehicle is saved. These values are used to update the points in the coordinate system of the grid map. With this method, the grid map is shifted in such a way, that no offset is caused during tracking grid map with the vehicle ...
Context 2
... model and visualize the environment around the vehicle in any places, the grid map coordinate system needs to move with the ego vehicle like in method 2. Meanwhile, some modifications are applied to solve the offset problem. According to the vehicle position, the grid map is just shifted with integer rows and columns in x-and y-direction. The rest difference between the origin point of the grid map and the ego position and is retained (see Figure 4). The orientation of the grid map is fixed by using the ego vehicle direction from the first measurement. During the vehicle motion the grid map is not rotated, instead the orientation of the ego vehicle is saved. These values are used to update the points in the coordinate system of the grid map. With this method, the grid map is shifted in such a way, that no offset is caused during tracking grid map with the vehicle ...
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Citations
... In addition to 2D grid maps, [158]- [160] focused on segmenting vehicles using 3D occupancy grid maps, in which the third dimension is encapsulated by height information and represented through distinct colors in the respective grid. Apart from segmentation on vehicles, [161], [162] utilized grid maps to segment the free-space of drivable areas, which are valuable to vehicle trajectory planning. ...
Driven by deep learning techniques, perception technology in autonomous driving has developed rapidly in recent years. To achieve accurate and robust perception capabilities, autonomous vehicles are often equipped with multiple sensors, making sensor fusion a crucial part of the perception system. Among these fused sensors, radars and cameras enable a complementary and cost-effective perception of the surrounding environment regardless of lighting and weather conditions. This review aims to provide a comprehensive guideline for radar-camera fusion, particularly concentrating on perception tasks related to object detection and semantic segmentation. Based on the principles of the radar and camera sensors, we delve into the data processing process and representations, followed by an in-depth analysis and summary of radar-camera fusion datasets. In the review of methodologies in radar-camera fusion, we address interrogative questions, including "why to fuse", "what to fuse", "where to fuse", "when to fuse", and "how to fuse", subsequently discussing various challenges and potential research directions within this domain. To ease the retrieval and comparison of datasets and fusion methods, we also provide an interactive website: https://XJTLU-VEC.github.io/Radar-Camera-Fusion.
... To mitigate the problem caused by sparse detection, Prophet et al. [9] implemented an adaption to the classic occupancy grid map algorithm that determines the extra free space between adjacent detections. Li et al. [34] utilized clustering algorithms to filter the noise and clutter. Slutsky et al. [35] extended the common ISM to positive and negative components, and the negative component mitigated noise signals. ...
Precise road scene understanding is of great essence to autonomous driving. As a widely used method for road scene understanding, occupancy grid mapping is leveraged to detect obstacles and predict drivable road areas. Because of its robustness under harsh conditions, low cost, and large perceptual range, radar sensor is becoming increasingly important to achieve various critical perception tasks. However, for radar-based occupancy grid mapping, current inverse sensor model ISM relies on detection data and is most hand-crafted. In this work, we propose a novel data-driven ISM that employs the range- Doppler matrix as the input. With a systematic evaluation and comparison of our model with classic, hand-crafted ISM and the data-driven, detection-based Occupancy Net using RADIal dataset, we find that data-driven models are far superior to their hand-crafted counterpart. Furthermore, although both data-driven models are on par within near range (
$< 50 \,\mathrm{m}$
), our model outperforms Occupancy Net by a large margin in far range (
$ [50 \,\mathrm{m},100 \,\mathrm{m}]$
). Specifically, our model has about a
0.3
and
0.4
improvement in distant range for the intersection over union IoU and
$\mathrm{F}_{1}$
score, respectively. In addition, leveraging adjacent occupancy grid map prediction, we propose a radar-based occupancy flow to precisely distinguish moving objects.
... This has been employed over the past 20 years as a development tool in a variety of industries, including forecasting, healthcare, security, and it has also considerably enhanced the performance of both production and service systems Artificial Intelligence is used for a variety of ways for training the soldiers in various military operations. Different armies have already begun launching various sensor simulation programmes [24][25][26]. Artificial intelligence will provide reasoning with the help of comparable external facts, internal factors, accumulated knowledge from the past and model an algorithm to apply it to our problems. This information can help identify any illegal or suspicious conduct and notify the proper authorities. ...
... Based on the movement of the ego vehicle, the a posteriori occupancy probability is computed to produce the occupancy grid map. In the binary grid map produced after the occupancy grid map, the grids in the obstacle regions are classified as occupied [26], [69][70][71][72][73]. Research Publish Journals Range-Doppler-Azimuth map is like a 3D data cube. Range-velocity is shown by the first two dimensions, while target location is indicated by the third dimension i.e., azimuth angle. ...
As we know that surveillance in the air has become one of the important defence precautions that we must take. So, for that reason radar plays a very important role as it captures images that can fool a human eye and then converts it into process able data. Then this data can be sorted and classified by our Artificial Intelligence Algorithms. Even here we have a variety of models for this purpose but it is up to us and our data and the output we expect, which Algorithm should be used. Here in this Survey, we have researched on different Radar Systems as well as the different signals that it generates and many AI Algorithms that are currently used in the industry.
... A lot of algorithms have been proposed to deeply investigate occupancy mapping and free space detection. Traditionally, occupancy grid mapping is performed by applying Bayesian filtering and using hand-crafted inverse sensor model (ISM) functions [3]- [12]. However, the main drawback of occupancy grid map is the high memory consumption when forming a fine-resolution grid for a large map. ...
... For LiDAR occupancy grid generation [9], a delta function ISM is typically used, while it is more common to see a Gaussian variant (in range and azimuth) of the delta function ISM when generating radar occupancy grid since radar data is sparser and noisier. In [10], [12], the Gaussian ISM was upgraded to rate detection probability by calculating the plausibility of range, angle, and amplitude for each measurement. In [3], ISM works with ego-motion velocity-dependent parameters by giving higher values for the uncertainty ellipses when there is higher ego-velocity leading to less detection. ...
... Trackable to old vertex: This means vertex e t is not from E t−1 and is close to the latest location of the previous vertex e t−1 for the same sampling sector within threshold 2 . If it satisfies, the following ISM Eq. (3) would be used to update its posterior probability t based on the Bayes' theorem [12]. ...
Inferring the drivable area in a scene is a key capability for ensuring vehicle avoids obstacles and enabling safe autonomous driving. However, traditional occupancy grid map suffers from high memory consumption when forming a fine-resolution grid for a large map. In this paper, we propose a lightweight, accurate, and predictable occupancy representation for automotive radars working for short-range applications that take interest in instantaneous free space surrounding the sensor. This new occupancy format is a polygon composed of a bunch of vertexes selected from radar measurements, which covers free space inside and gives a Doppler moving velocity for each vertex. It not only takes a very small memory for storage and update at every timeslot, but also has the predictable shape-change property based on vertex Doppler velocity. We name this kind of occupancy representation `deformable radar polygon'. Two formation algorithms for radar polygon are introduced for both single timeslot and continuous ISM update. To fit this new polygon representation, a matrix-form collision detection method have been modeled as well. The radar polygon algorithms and collision detection model have been validated via extensive experiments with real collected data and simulations, showing that the deformable radar polygon is very competitive in terms of its completeness, smoothness, accuracy, lightweight as well as shape-predictable property. Our codes will be made publicly available for ease of future works.
... Free space detection is being studied not only for the study of autonomous vehicle driving, but also in various fields such as the recognition of a parking space or the driving of an aircraft [25][26][27]. Among them, the research trends in the detection of free space in autonomous vehicles are as follows [1][2][3][4][5][6][7][8][9][10][11][12]. Often, a camera or LiDAR sensor is used, or both sensors are used, to recognize the space and obstacles around the vehicle. ...
... J. Tao [3] used LiDAR sensor data to detect free space, in which free space was also detected without information about the speed of the obstacle. Many previous studies used static data without considering the speed of obstacles [1][2][3][4][5][6][7][8][9][10][11][12]. Free space detection for autonomous driving must also be considered in dynamic environments. ...
... To supplement this, in this paper, we propose a method to detect free space by reflecting the velocity data of obstacles. Table 1 summarizes existing papers [1][2][3][4][5][6][7][8][9][10][11][12]. In previous papers, sensors were used in the FSD process, and the speed information of obstacles was summarized. ...
In this paper, we propose a new free space detection algorithm for autonomous vehicle driving. Previous free space detection algorithms often use only the location information of every frame, without information on the speed of the obstacle. In this case, there is a possibility of creating an inefficient path because the behavior of the obstacle cannot be predicted. In order to compensate for the shortcomings of the previous algorithm, the proposed algorithm uses the speed information of the obstacle. Through object tracking, the dynamic behavior of obstacles around the vehicle is identified and predicted, and free space is detected based on this. In the free space, it is possible to classify an area in which driving is possible and an area in which it is not possible, and a route is created according to the classification result. By comparing and evaluating the path generated by the previous algorithm and the path generated by the proposed algorithm, it is confirmed that the proposed algorithm is more efficient in generating the vehicle driving path.
... 21 a-d 70565, Stuttgart, Germany Full list of author information is available at the end of the article values is separating the perception task for stationary and moving objects [3,4]. In the static world, radar data can be accumulated over long time frames, because moving objects can be filtered out via their Doppler signature, thus alleviating the data sparsity problem [5][6][7]. Owing to their dynamic nature, moving objects require smaller time frames, typically within a few hundred milliseconds. Most detection and classification methods require an accumulation of several measurement cycles to increase the point cloud's density as depicted in Fig. 1. ...
Automotive radar perception is an integral part of automated driving systems. Radar sensors benefit from their excellent robustness against adverse weather conditions such as snow, fog, or heavy rain. Despite the fact that machine-learning-based object detection is traditionally a camera-based domain, vast progress has been made for lidar sensors, and radar is also catching up. Recently, several new techniques for using machine learning algorithms towards the correct detection and classification of moving road users in automotive radar data have been introduced. However, most of them have not been compared to other methods or require next generation radar sensors which are far more advanced than current conventional automotive sensors. This article makes a thorough comparison of existing and novel radar object detection algorithms with some of the most successful candidates from the image and lidar domain. All experiments are conducted using a conventional automotive radar system. In addition to introducing all architectures, special attention is paid to the necessary point cloud preprocessing for all methods. By assessing all methods on a large and open real world data set, this evaluation provides the first representative algorithm comparison in this domain and outlines future research directions.
... Standard occupancy grid map methods have been derived for the LiDAR sensor model. Adaptions for specific radar sensor models are described in [40]- [42]. ...
... Freespace estimation and road scene understanding is presented with a semantic segmentation CNN in [56]. Lane marking detection using a radar grid map is presented in [42]. A visionary approach, which uses dedicated radar targets as lane markers, is suggested in [57]. ...
Automotive radar is used in many applications of
advanced driver assistance systems and is considered as one of
the key technologies for highly automated driving. An overview of
state-of-the-art signal processing in automotive radar is presented
along with current research directions and practical challenges.
We provide a comprehensive signal model for the multipletarget
case using multi-input multi-output schemes, and discuss
a practical processing chain to calculate the target list. To
demonstrate the capabilities of a modern series production highperformance
radar sensor, real data examples are given. An
overview of conventional target processing and recent research
activities in machine learning and deep learning approaches is
presented. Additionally, recent methods for practically relevant
radar-camera fusion are discussed.
... To build occupancy grid maps and to detect free space, we follow the state-of-the-art approach proposed by Li et al. [40]. First, using an artificial neural network adopting back-propagation, we translate sensor reading into occupancy values. ...
Research on autonomous cars has become one of the main research paths in the automotive industry, with many critical issues that remain to be explored while considering the overall methodology and its practical applicability. In this paper, we present an industrial experience in which we build a complete autonomous driving system, from the sensor units to the car control equipment, and we describe its adoption and testing phase on the field. We report how we organize data fusion and map manipulation to represent the required reality. We focus on the communication and synchronization issues between the data-fusion device and the path-planner, between the CPU and the GPU units, and among different CUDA kernels implementing the core local planner module. In these frameworks, we propose simple representation strategies and approximation techniques which guarantee almost no penalty in terms of accuracy and large savings in terms of memory occupation and memory transfer times. We show how we adopt a recent implementation on parallel many-core devices, such as CUDA-based GPGPU, to reduce the computational burden of rapidly exploring random trees to explore the state space along with a given reference path. We report on our use of the controller and the vehicle simulator. We run experiments on several real scenarios, and we report the paths generated with the different settings, with their relative errors and computation times. We prove that our approach can generate reasonable paths on a multitude of standard maneuvers in real time.
... This means occupancy grid maps allow for a distinction between free space, obstacles, and an unknown area, as shown in the example in Fig 22. In contrast to feature-based approaches, the free space and the occupied space is directly visible in such maps and does not have to be interpolated or calculated additionally [102], [103]. Due to the probabilistic cell representation, neither ghost targets are represented as real targets nor hidden targets as free space, which enables a highly accurate and very robust map. ...
Although the beginning of research on automotive radar sensors goes back to the 1960s, automotive radar has remained one of the main drivers of innovation in millimeter wave technology over the past two decades. Today, millions of sensors are produced each year, which was made possible by inexpensive and mature millimeter wave technology. The technology maturity, in turn, enables research to be carried out on systems that are considerably more complex and powerful than was possible just a few years ago. The focus of research has thus shifted from purely hardware-oriented and device-level topics to sophisticated millimeter wave systems and RF signal processing topics. This opens up new research topics such as digital modulation schemes, radar networks, radar imaging, and machine learning. In this review paper, we sketch the path from the very beginning through the state of the art with sophisticated multiple-input multiple-output (MIMO) antenna arrays and mature assembly and interconnect concepts to today's key research topics of automotive radar.
... The free space is defined by the narrowest distance between the vehicle possible position and the border of the occupied space. Based on radar grid maps describing the static environment, free space can be further determined based on the border recognition algorithm [34]. Compared with LiDAR, occupied objects can be better detected, and a more accurate free space range can be obtained with radar due to its penetrability [33]. ...
With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.
