Automotive Camera and Integrated Radar and Camera Market Size, Share & Trends Analysis Report by Type (Automotive camera and Integrated radar and camera), By View Type (Front view, Rearview and Surround view), By Vehicle Type (Passenger cars, Commercial vehicles and Heavy-commercial vehicles), By Application (ADAS and Park assist and viewing) and By Region - Market Scope, Global Growth Opportunities, Threats & Industry Research Forecast, 2021-2028

Automotive Camera and Integrated Radar Market Outlook

The global automotive camera and integrated radar market size is projected to grow from $5.2 billion in 2023 to $12.3 billion by 2032, at a compound annual growth rate (CAGR) of 10.2%. This growth is driven by advancements in autonomous driving technologies, safety regulations, and consumer demand for enhanced vehicle safety features. As the automotive industry shifts towards advanced driver-assistance systems (ADAS) and autonomous vehicles, the integration of cameras and radar systems becomes increasingly crucial, driving the market further.

One of the primary growth factors for the automotive camera and integrated radar market is the increasing adoption of ADAS technologies in both passenger and commercial vehicles. Regulatory bodies across the globe are enforcing stringent safety standards that mandate the inclusion of features like lane departure warning, automatic emergency braking, and adaptive cruise control. These systems rely heavily on high-precision cameras and radar to provide real-time data and enhance vehicle safety. This regulatory push is particularly strong in regions such as Europe and North America, where road safety norms are highly stringent, thereby fueling market growth.

Another significant driver is the surge in consumer demand for enhanced vehicle safety and convenience features. Modern consumers are increasingly aware of the benefits provided by ADAS technologies, such as reduced risk of accidents and improved driving experience. Consequently, automakers are focusing on integrating advanced camera and radar systems to differentiate their offerings and meet consumer expectations. The rise of electric vehicles (EVs) and the push towards autonomous driving further amplify this trend, as these vehicles require sophisticated sensory technologies to operate efficiently and safely.

Technological advancements in imaging and radar technologies are also propelling the market forward. Improvements in camera resolution, radar accuracy, and data processing capabilities are enabling more reliable and versatile ADAS solutions. The integration of machine learning and artificial intelligence (AI) allows these systems to better interpret sensory data and make more informed decisions, enhancing the overall performance of ADAS. This technological progress is not only making these systems more effective but also more affordable, thereby widening their adoption across different vehicle segments.

Regionally, the Asia Pacific market is expected to witness substantial growth, driven by the booming automotive industry in countries like China, Japan, and South Korea. These nations are not only leading in vehicle production but are also at the forefront of adopting advanced automotive technologies. Government initiatives to promote EVs and improve road safety standards are further boosting the demand for automotive cameras and integrated radar systems in this region. Additionally, the presence of key market players and a robust manufacturing ecosystem contribute to the region's dominance.

Product Type Analysis

The automotive camera and integrated radar market is segmented by product type into front view cameras, rear view cameras, surround view cameras, and integrated radar and camera systems. Each of these product types plays a vital role in enhancing vehicle safety and performance. Front view cameras are primarily used for lane departure warning systems, forward collision warning, and adaptive cruise control. These cameras provide a clear view of the road ahead, enabling the vehicle to detect obstacles, lane markings, and traffic signs. The increasing demand for these safety features in both luxury and economy vehicles is driving the growth of the front view camera segment.

Rear view cameras, on the other hand, are essential for parking assistance and reversing safety. They are widely adopted in passenger vehicles to help drivers avoid obstacles while reversing, thereby reducing the risk of collisions. The growing trend of incorporating these cameras in commercial vehicles, such as trucks and buses, to enhance driver visibility and safety is also contributing to the segment's growth. The introduction of regulations mandating the use of rear view cameras in new vehicles in several countries is further propelling the market.

Surround view cameras provide a 360-degree view around the vehicle, significantly enhancing situational awareness and safety. These systems are particularly useful in crowded urban environments and for parking in tight spaces. The increasing

Camera Radar Fusion 3

## Overview

Camera Radar Fusion 3 is a dataset for instance segmentation tasks - it contains Objects WcJk annotations for 1,042 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The automotive integrated radar camera market is projected to reach a value of $674.52 million by 2033, growing at a CAGR of 8.82% during the forecast period 2025-2033. The market growth is attributed to the increasing demand for advanced driver assistance systems (ADAS) and autonomous vehicles. ADAS features such as adaptive cruise control, lane departure warning, and collision avoidance systems rely on radar and camera data to operate. The integration of radar and camera provides a more comprehensive view of the vehicle's surroundings, improving the accuracy and reliability of these systems. Key drivers of the market include:

Growing demand for ADAS and autonomous vehicles Increasing awareness of safety features Government regulations mandating the use of certain safety features Advances in radar and camera technology Decreasing cost of radar and camera sensors

Key trends in the market include:

Integration of radar and camera data for enhanced ADAS functionality Development of new radar and camera sensors with improved performance Increasing use of AI and machine learning for radar and camera data processing Key drivers for this market are: Increased demand for ADAS Growth in autonomous vehicle production Advancements in radar technology Rising safety regulations globally Expansion of smart city initiatives. Potential restraints include: Growing demand for advanced safety, rapid technological advancements; increasing vehicle automation; stringent government regulations; and rising consumer awareness.

Camera Radar Fusion

## Overview

Camera Radar Fusion is a dataset for instance segmentation tasks - it contains Objects annotations for 1,042 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The automotive camera and integrated radar market is projected to grow from USD 36.6 billion in 2025 to USD 118.9 billion by 2033, at a CAGR of 16.1% during the forecast period. The increasing demand for advanced safety features and the growing adoption of autonomous driving technologies are the primary drivers of this growth. The automotive camera and integrated radar market is segmented by application and type. Based on application, the market is divided into safety and security and infotainment. Based on type, the market is divided into cameras and radars. The cameras segment is further sub-segmented into surround view cameras, rear-view cameras, and night vision cameras. The radars segment is further sub-segmented into short-range radars, medium-range radars, and long-range radars. The major companies operating in this market include Robert Bosch GmbH, Continental AG, Valeo SA, Aptiv PLC, Magna Corporation, Intel Corporation, Infineon Technologies AG, ZF Friedrichshafen, and Veoneer Inc.

Dataset Introduction The advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is arguably the most commonly explored application. Nevertheless, more suitable benchmarking datasets than currently available are needed to assess and compare the merits of the different proposed solutions and explore a broader range of scenarios than simple hand-gesturing a few centimeters away from a radar transmitter/receiver. Most current publicly available radar datasets used in gesture recognition provide limited diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and make available a new dataset that combines FMCW radar and dynamic vision camera of 10 aircraft marshalling signals (whole body) at several distances and angles from the sensors, recorded from 13 people. The two modalities are hardware synchronized using the radar's PRI signal. Moreover, in the supporting publication we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks), and can be used to create an sparse event-based representation of the radar data. In this way the dataset can be used as a two-modality neuromorphic dataset. Synchronization of the two modalities The PRI pulses from the radar have been hard-wired to the event stream of the DVS sensor, and timestamped using the DVS clock. Based on this signal the DVS event stream has been segmented such that groups of events (time-bins) of the DVS are mapped with individual radar pulses (chirps). Data storage DVS events (x,y coords and timestamps) are stored in structured arrays, and one such structured array object is associated with the data of a radar transmission (pulse/chirp). A radar transmission is a vector of 512 ADC levels that correspond to sampling points of chirping signal (FMCW radar) that lasts about ~1.3ms. Every 192 radar transmissions are stacked in a matrix called a radar frame (each transmission is a row in that matrix). A data capture (recording) consisting of some thousands of continuous radar transmissions is therefore segmented in a number of radar frames. Finally radar frames and the corresponding DVS structured arrays are stored in separate containers in a custom-made multi-container file format (extension .rad). We provide a (rad file) parser for extracting the data out of these files. There is one file per capture of continuous gesture recording of about 10s. Note the number of 192 transmissions per radar frame is an ad-hoc segmentation that suits the purpose of obtaining sufficient signal resolution in a 2D FFT typical in radar signal processing, for the range resolution of the specific radar. It also served the purpose of fast streaming storing of the data during capture. For extracting individual data points for the dataset however, one can pool together (concat) all the radar frames from a single capture file and re-segment them according to liking. The data loader that we provide offers this, with a default of re-segmenting every 769 transmissions (about 1s of gesturing). Data captures directory organization (radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z) The dataset captures (recordings) are organized in a common directory structure which encompasses additional metadata information about the captures. dataset_dir///--/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.rad Identifiers

stage [train, test]. room: [conference_room, foyer, open_space]. subject: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test). gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right']. distance: 'xxx', '100', '150', '200', '250', '300', '350', '400', '450'. Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking in front of the radar with varying distances but performing no gesture. The test data captures contain both subjects that appear in the train data as well as previously unseen subjects. Similarly the test data contain captures from the spaces that train data were recorded at, as well as from a new unseen open space. Files List radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z This is the actual archive bundle with the data captures (recordings). rad_file_parser_2.py Parser for individual .rad files, which contain capture data. loader.py A convenience PyTorch Dataset loader (partly Tonic compatible). You practically only need this to quick-start if you don't want to delve too much into code reading. When you init a DvsRadarAircraftMarshallingSignals class object it automatically downloads the dataset archive and the .rad file parser, unpacks the archive, and imports the .rad parser to load the data. One can then request from it a training set, a validation set and a test set as torch.Datasets to work with.
aircraft_marshalling_signals_howto.ipynb Jupyter notebook for exemplary basic use of loader.py Contact For further information or questions try contacting first M. Sifalakis or F. Corradi.

Automotive Camera and Integrated Radar and Camera Market Outlook

According to our latest research, the global Automotive Camera and Integrated Radar and Camera market size reached USD 12.7 billion in 2024, reflecting robust adoption across the automotive industry. The market is projected to expand at a CAGR of 10.2% from 2025 to 2033, culminating in a forecasted market size of USD 30.5 billion by 2033. This impressive growth trajectory is fueled by the increasing integration of advanced driver assistance systems (ADAS) and the rising demand for vehicle safety and automation features worldwide.

A key growth factor propelling the Automotive Camera and Integrated Radar and Camera market is the intensifying regulatory focus on road safety and the mandatory adoption of advanced safety technologies in both developed and emerging economies. Governments across North America, Europe, and Asia Pacific are enacting stringent vehicle safety standards that require the inclusion of ADAS features such as lane departure warning, blind spot detection, and collision avoidance systems. These regulations have accelerated the integration of automotive cameras and radar-based sensors, as automakers strive to comply with legislative requirements and enhance consumer safety. Furthermore, the proliferation of high-profile road accidents and the growing public awareness of automotive safety have further reinforced the demand for these advanced sensor technologies.

Another significant growth driver is the rapid technological advancement in imaging and radar technologies, which has dramatically improved the performance and affordability of automotive cameras and integrated radar systems. Innovations such as high-resolution mono and stereo cameras, surround view systems, and the evolution of short- and long-range radar have enabled more precise object detection, real-time data processing, and robust performance under diverse driving conditions. These advancements are not only enhancing the capabilities of premium vehicles but are also making advanced safety features accessible in mid-range and entry-level vehicles, thereby expanding the market’s addressable base. The continuous R&D investments by leading automotive suppliers and technology firms are expected to further accelerate the adoption of integrated radar and camera solutions in the coming years.

Additionally, the ongoing shift toward electric and autonomous vehicles is significantly influencing the Automotive Camera and Integrated Radar and Camera market. Electric vehicles (EVs) and next-generation autonomous vehicles rely heavily on an array of sensors, including cameras and radar, to enable functions such as automated parking, adaptive cruise control, and comprehensive situational awareness. As OEMs and technology companies intensify their efforts to commercialize autonomous driving technologies, the demand for integrated sensor platforms is expected to surge. This trend is further bolstered by strategic collaborations, M&A activities, and the entry of new players specializing in sensor fusion and artificial intelligence, all of which are contributing to the dynamic evolution of the market landscape.

From a regional perspective, Asia Pacific continues to dominate the global Automotive Camera and Integrated Radar and Camera market, accounting for the largest revenue share in 2024. This leadership is underpinned by the presence of major automotive manufacturing hubs, the rapid adoption of advanced technologies in countries like China, Japan, and South Korea, and favorable government policies supporting vehicle electrification and safety enhancements. North America and Europe also represent significant markets, driven by high consumer demand for premium vehicles and early adoption of ADAS technologies. Meanwhile, emerging markets in Latin America and the Middle East & Africa are witnessing gradual growth, supported by rising vehicle ownership and increasing awareness of road safety.

Product Type Analysis

The Autom

Radar-Camera depth estimation aims to predict dense and accurate metric depth by fusing input images and Radar data. Model efficiency is crucial for this task in pursuit of real-time processing on autonomous vehicles and robotic platforms. However, due to the sparsity of Radar returns, the prevailing methods adopt multi-stage frameworks with intermediate quasi-dense depth, which are time-consuming and not robust. To address these challenges, we propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion. Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed to capture and integrate the graph structures of Radar point clouds, delivering superior model efficiency and robustness without relying on the intermediate depth results. Moreover, TacoDepth can be flexible for different inference modes, providing a better balance of speed and accuracy. Extensive experiments are conducted to demonstrate the efficacy of our method. Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%. Our work provides a new perspective on efficient Radar-Camera depth estimation.

The automotive sensor market, encompassing radar, lidar, and camera technologies, is experiencing robust growth driven by the increasing demand for Advanced Driver-Assistance Systems (ADAS) and autonomous driving capabilities. The market's expansion is fueled by several key factors: rising vehicle production globally, stricter safety regulations mandating ADAS features, and continuous technological advancements leading to more affordable and sophisticated sensor solutions. While radar remains the dominant technology due to its maturity and cost-effectiveness in short-range applications, lidar is rapidly gaining traction for its superior long-range object detection and 3D mapping capabilities, crucial for higher levels of autonomy. Camera systems, although mature, continue to improve with higher resolutions, wider fields of view, and advanced image processing algorithms, enhancing their role in object recognition and driver monitoring. The competitive landscape is highly fragmented, with established automotive suppliers like Bosch, Continental, and Denso alongside emerging lidar and sensor fusion specialists like Luminar and Velodyne vying for market share. Growth is expected to be particularly strong in regions with rapidly expanding automotive industries and supportive government policies promoting autonomous vehicle development. Looking ahead to 2033, the market is projected to witness substantial growth, particularly in the lidar segment due to its increasing adoption in higher-level autonomous driving systems. Challenges remain, however, including the high cost of lidar technology, the need for robust sensor fusion algorithms to integrate data from multiple sensor types, and the ongoing development of robust safety standards for autonomous vehicles. Furthermore, the dependence on complex supply chains and potential chip shortages could impact market growth. Nevertheless, continuous innovation in sensor technology, coupled with increasing consumer demand for safer and more convenient vehicles, points towards a sustained period of growth for the automotive radar, lidar, and camera market.

This is a supplementary dataset, which is linked to https://zenodo.org/record/7088054#.YyVF3ehBwQ8. The dataset is composed of corner radar point cloud data and front radar point cloud data collected from environments existing obstacles between volunteers and sensors.

Overview The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data contain video data of the wind turbine blades during operation as well as while parked. Since the blades had a coning angle, the blades are only in frame of the video camera when the blades are in a pitched to run configuration. Data Details For photogrammetry calibration of the video data, the following can be used: Blade 1, 8 meter outboard target: 1.43 mm/pixel; Blade 1, 13 meter outboard target: 2.23 mm/pixel; Blade 1, tip: 3.17 mm/pixel; Blade 2, 8 meter outboard target: 1.6 mm/pixel; Blade 2, 13 meter outboard target: 2.67 mm/pixel; Blade 2, tip: 3.72 mm/pixel. This calibration provides the span. For the offset, the attachment explains how to obtain an offset calibration when the blade is offloaded.

The RadarScenes data set (“data set”) contains recordings from four automotive radar sensors, which were mounted on one measurement-vehicle. Images from one front-facing documentary camera are added.

The data set has a length of over 4h and in addition to the point cloud data from the radar sensors, semantic annotations on a point-wise level from 12 different classes are provided.

In addition to point-wise class labels, a track-id is attached to each individual detection of a dynamic object, so that individual objects can be tracked over time.

Structure of the Data Set

The data set consist of 158 individual sequences. For each sequence, the recorded data from radar and odometry sensors are stored in one hdf5 file. Each of these files is accompanied by a json file called “scenes.json” in which meta-information are stored. In a subfolder, the camera images are stored as jpg files.

Two additional json files give further meta-information: in the "sensor.json" file, the sensor mounting position and rotation angles are defined. In the file "sequences.json", all recorded sequences are listed with additional information, e.g. about the recording duration.

sensors.json

This file describes the position and orientation of the four radar sensors. Each sensor is attributed with an integer id. The mounting position is given relative to the center of the rear axle of the vehicle. This allows for an easier calculation of the ego-motion at the position of the sensors. Only the x and y position is given, since no elevation information is provided by the sensors. Similarly, only the yaw-angle for the rotation is needed.

sequences.json

This file contains one entry for each recorded sequence. Each entry is built from the following information: the category (training or validation of machine learning algorithms), the number of individual scenes within the sequence, the duration in seconds and the names of the sensors which performed measurements within this sequence.

scenes.json

In this file, meta-information for a specific sequence and the scenes within this sequence are stored.

The name of the sequence is listed within the top-level dictionary, the group of this sequence (training or validation) as well as the timestamps of the first and last time a radar sensor performed a measurement in this sequence.

A scene is defined as one measurement of one of the four radar sensors. For each scene, the sensor id of the respective radar sensor is listed. Each scene has one unique timestamp, namely the time at which the radar sensor performed the measurement. Four timestamps of different radar measurement are given for each scene: the next and previous timestamp of a measurement of the same sensor and the next and previous timestamp of a measurement of any radar sensor. This allows to quickly iterate over measurements from all sensors or over all measurements of a single sensor. For the association with the odometry information, the timestamp of the closest odometry measurement and additionally the index in the odometry table in the hdf5 file where this measurement can be found are given. Furthermore, the filename of the camera image whose timestamp is closest to the radar measurement is given. Finally, the start and end indices of this scene’s radar detections in the hdf5 data set “radar_data” is given. The first index corresponds to the row in the hdf5 data set in which the first detection of this scene can be found. The second index corresponds to the row in the hdf5 data set in which the next scene starts. That is, the detection in this row is the first one that does not belong to the scene anymore. This convention allows to use the common python indexing into lists and arrays, where the second index is exclusive: arr[start:end].

radar_data.h5

In this file, both the radar and the odometry data are stored. Two data sets exists within this file: “odometry” and “radar_data”.

The “odometry” data has six columns: timestamp, x_seq, y_seq, yaw_seq, vx, yaw_rate. Each row corresponds to one measurement of the driving state. The columns x_seq, y_seq and yaw_seq describe the position and orientation of the ego-vehicle relative to some global origin. Hence, the pose in a global (sequence) coordinate system is defined. The column “vx” contains the velocity of the ego-vehicle in x-direction and the yaw_rate column contains the current yaw rate of the car.

The hdf5 data set “radar_data” is composed of the individual detections. Each row in the data set corresponds to one detection. A detection is defined by the following signals, each being listed in one column:

• timestamp: in micro seconds relative to some arbitrary origin
• sensor_id: integer value, id of the sensor that recorded the detection
• range_sc: in meters, radial distance to the detection, sensor coordinate system
• azimuth_sc: in radians, azimuth angle to the detection, sensor coordinate system
• rcs: in dBsm, RCS value of the detection
• vr: in m/s. Radial velocity measured for this detection
• vr_compensated in m/s: Radial velocity for this detection but compensated for the ego-motion
• x_cc and y_cc: in m, position of the detection in the car-coordinate system (origin is at the center of the rear-axle)
• x_seq and y_seq in m, position of the detection in the global sequence-coordinate system (origin is at arbitrary start point)
• uuid: unique identifier for the detection. Can be used for association with predicted labels and debugging
• track_id: id of the dynamic object this detection belongs to. Empty, if it does not belong to any.
• label_id: semantic class id of the object to which this detection belongs. passenger cars (0), large vehicles (like agricultural or construction vehicles) (1), trucks (2), busses (3), trains (4), bicycles (5), motorized two-wheeler (6), pedestrians (7), groups of pedestrian (8), animals (9), all other dynamic objects encountered while driving (10), and the static environment (11)

Camera Images

The images of the documentary camera are located in the subfolder “camera” of each sequence. The filename of each image corresponds to the timestamp at which the image was recorded.

The data set is a radar data set. Camera images are only included so that users of the data set get a better understanding of the recorded scenes. However, due to GDPR requirements, personal information was removed from these images via re-painting of regions proposed by a semantic instance segmentation network and manual correction. The networks were optimized for high recall values so that false-negatives were suppressed at the cost of having false positive markings. As the camera images are only meant to be used as guidance to the recorded radar scenes, this shortcoming has no negative effect on the actual data.

Tools

Some helper tools - including a viewer - can be found in the python package radar_scenes. Details can be found here: https://github.com/oleschum/radar_scenes

Publications

Previous publications related to classification algorithms on radar data already used this data set:

• Scene Understanding With Automotive Radar; https://ieeexplore.ieee.org/document/8911477

• Semantic Segmentation on Radar Point Clouds, https://ieeexplore.ieee.org/document/8455344

• Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?, https://ieeexplore.ieee.org/document/9190338

• Detection and Tracking on Automotive Radar Data with Deep Learning, https://ieeexplore.ieee.org/document/9190261

• Comparison of random forest and long short-term memory network performances in classification tasks using radar, https://ieeexplore.ieee.org/document/8126350

License

The data set is licensed under Creative Commons Attribution Non Commercial Share Alike 4.0 International (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). Hence, the data set must not be used for any commercial use cases.

Disclaimer

That the data set comes "AS IS", without express or implied warranty and/or any liability exceeding mandatory statutory obligations. This especially applies to any obligations of care or indemnification in connection with the data set. The annotations were created for our research purposes only and no quality assessment was done for the usage in products of any kind. We can therefore not guarantee for the correctness, completeness or reliability of the provided data set.

The eruption of Eyjafjallajökull volcano in 2010 lasted for 39 days, 14 April–23 May. The eruption had two explosive phases separated by a phase with lava formation and reduced explosive activity. The height of the plume was monitored every 5 min with a C-band weather radar located in Keflavík International Airport, 155 km distance from the volcano. Furthermore, several web cameras were mounted with a view of the volcano, and their images saved every five seconds. Time series of the plume-top altitude were constructed from the radar observations and images from a web camera located in the village Hvolsvöllur at 34 km distance from the volcano. This paper presents the independent radar and web camera time series and performs cross validation.

This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit http://en.eradartech.com/ to get more information.

The global automotive camera and integrated radar market is experiencing robust growth, driven by increasing demand for advanced driver-assistance systems (ADAS) and autonomous driving features. The market, estimated at $25 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value of $80 billion by 2033. This significant expansion is fueled by several key factors. Stringent government regulations mandating ADAS features in new vehicles are a major catalyst. Furthermore, the rising consumer preference for enhanced safety and convenience features, coupled with technological advancements leading to smaller, more cost-effective sensor solutions, are boosting market penetration. The increasing integration of radar and camera systems within vehicles for improved object detection and situational awareness is further propelling market growth. Segmentation analysis reveals that the front view camera segment currently holds the largest market share, followed by surround view and rearview systems. Within applications, passenger cars dominate the market, although commercial vehicles are showing considerable growth potential, particularly in the heavy-commercial vehicle segment. Key players like Robert Bosch, Continental, and Valeo are actively investing in research and development to maintain their competitive edge. The market's regional distribution reveals North America and Europe as leading consumers, reflecting the high adoption rates of ADAS in these regions. However, the Asia-Pacific region is expected to witness the fastest growth due to increasing vehicle production and rising disposable incomes. The competitive landscape is characterized by the presence of both established automotive suppliers and technology companies. These players are engaged in strategic partnerships, mergers, and acquisitions to expand their market reach and technological capabilities. While the market enjoys a positive outlook, challenges remain. High initial costs associated with implementing ADAS and autonomous driving technologies could hinder adoption, particularly in developing economies. Additionally, ensuring data security and privacy related to the vast amount of data generated by these systems is a critical concern that needs to be addressed. Future market growth will depend on overcoming these challenges and on the continued technological advancements in sensor fusion algorithms and artificial intelligence that improve the accuracy and reliability of ADAS systems. The automotive camera and integrated radar market is poised for continued expansion, making it an attractive investment opportunity for companies with the right technology and strategic vision.

The Automotive Camera and Integrated Radar market is anticipated to witness substantial growth over the coming years, reaching a value of XXX million by 2033. This growth is driven by increasing demand for advanced driver assistance systems (ADAS), rising awareness about road safety, and stringent government regulations mandating the installation of such systems in new vehicles. Key trends shaping the market include the adoption of artificial intelligence (AI) and machine learning (ML) for image processing, the integration of radar sensors with cameras for improved accuracy and reliability, and the development of surround-view camera systems for enhanced visibility. Key players in the Automotive Camera and Integrated Radar market include Robert Bosch GmbH, Continental AG, Valeo SA, Aptiv PLC, Magna Corporation, Intel Corporation, Infineon Technologies AG, ZF Friedrichshafen, and Veoneer Inc. These companies are focusing on strategic partnerships, acquisitions, and product innovation to gain a competitive edge. The market is fragmented, with a mix of global players and regional suppliers. Asia-Pacific is expected to hold a dominant share of the market, followed by North America and Europe. The increasing adoption of ADAS and stringent government regulations in these regions are driving growth. This growth is also supported by the presence of major automotive manufacturers and a large consumer base.

Images from the sky camera mounted at the Natural Environment Research Council's (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility, Capel Dewi, near Aberystwyth in West Wales. Images are in jpeg format.

This dataset reflects the daily volume of violations that have occurred in Children's Safety Zones for each camera. The data reflects violations that occurred from July 1, 2014 until present, minus the most recent 14 days. This data may change due to occasional time lags between the capturing of a potential violation and the processing and determination of a violation. The most recent 14 days are not shown due to revised data being submitted to the City of Chicago. The reported violations are those that have been collected by the camera and radar system and reviewed by two separate City contractors. In some instances, due to the inability the registered owner of the offending vehicle, the violation may not be issued as a citation. However, this dataset contains all violations regardless of whether a citation was issued, which provides an accurate view into the Automated Speed Enforcement Program violations taking place in Children's Safety Zones. More information on the Safety Zone Program can be found here: http://www.cityofchicago.org/city/en/depts/cdot/supp_info/children_s_safetyzoneporgramautomaticspeedenforcement.html. The corresponding dataset for red light camera violations is https://data.cityofchicago.org/id/spqx-js37.

The presented dataset consists of raw and transformed CWT images of the breathing and heart waveforms obtained from a radar
and IP camera setup. The setup is a self-proposed setup with a mmWave radar mounted over an IP camera and can capture
the breathing and heart waveforms of the person in the room in front of the system in any orientation. The dataset can be used to estimate the vital signs using any machine learning model and important information about the respiration rate and pulse rate
can then be obtained. The dataset is a total of 1280 images- 720 raw and 720 processed. The processed dataset is labeled in six different classes. The first bifurcation is between breath and heart, breath signal is classified as low, normal, and high whereas the heart signal is classified as low, normal, and slightly low. The subject is oriented in differently so that the setup can steer towards the person and capture the breath and heart signals accordingly.