Computer Stereo Vision for Autonomous Driving

Fan, Rui; Wang, Li; Bocus, Mohammud Junaid; Pitas, Ioannis

doi:10.48550/arxiv.2012.03194

Cited by 5 publications

(6 citation statements)

References 76 publications

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“…The IP-Basic algorithm estimates additional depth measurements from lidar pixels, so that additional camera data can be used for the detection. The depth of these estimated pixels is less accurate than that of the lidar sensor, which is in compliance with the fact that stereo camera depth estimation is also more error-prone than that of lidar [36,37].…”

Section: Simulated Depth Cameramentioning

confidence: 61%

Radar Voxel Fusion for 3D Object Detection

et al. 2021

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Automotive traffic scenes are complex due to the variety of possible scenarios, objects, and weather conditions that need to be handled. In contrast to more constrained environments, such as automated underground trains, automotive perception systems cannot be tailored to a narrow field of specific tasks but must handle an ever-changing environment with unforeseen events. As currently no single sensor is able to reliably perceive all relevant activity in the surroundings, sensor data fusion is applied to perceive as much information as possible. Data fusion of different sensors and sensor modalities on a low abstraction level enables the compensation of sensor weaknesses and misdetections among the sensors before the information-rich sensor data are compressed and thereby information is lost after a sensor-individual object detection. This paper develops a low-level sensor fusion network for 3D object detection, which fuses lidar, camera, and radar data. The fusion network is trained and evaluated on the nuScenes data set. On the test set, fusion of radar data increases the resulting AP (Average Precision) detection score by about 5.1% in comparison to the baseline lidar network. The radar sensor fusion proves especially beneficial in inclement conditions such as rain and night scenes. Fusing additional camera data contributes positively only in conjunction with the radar fusion, which shows that interdependencies of the sensors are important for the detection result. Additionally, the paper proposes a novel loss to handle the discontinuity of a simple yaw representation for object detection. Our updated loss increases the detection and orientation estimation performance for all sensor input configurations. The code for this research has been made available on GitHub.

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Section: Simulated Depth Cameramentioning

confidence: 61%

Radar Voxel Fusion for 3D Object Detection

et al. 2021

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“…It can detect from 10-250 meters at the wide-range of ±15 • and recognize multiple objects simultaneously, which is particularly suitable for long distance forward obstacles detection and collision avoidance. Since the ability of remote obstacle recognition prepares enough range and time for braking, it is used in ADAS like automatic emergency braking and traffic jam assistance [34], which guarantees high speed safety. By measuring the reflection of electromagnetic waves, radar can also help in relative speed calculation, which ensures the adaptive cruise control (ACC) function.…”

Section: Radarmentioning

confidence: 99%

“…Ultrasonic sensor is the basic and numerous perception sensor mounted on the vehicle, owing to the lower price and robust performance. It transmits over 2000Hz frequency magnitude signal and calculates the source-object distance when receiving the echo [34].…”

Section: Ultrasonic Transceivermentioning

confidence: 99%

Composition and Application of Current Advanced Driving Assistance System: A Review

Li,

Lin,

Meng

et al. 2021

Preprint

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Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, and novel applications in real-world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility.This paper makes a general introduction about ADAS by analyzing its hardware support and computation algorithms. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about the definition of ADAS from different institutes are reviewed in this paper, and future approaches about ADAS in China are introduced in particular.

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“…Fan, Rui and collaborators discuss recent advancements in parallel computing architectures to enhance autonomous vehicle perception capabilities, focusing on computer stereo vision. The article provides a comprehensive overview of both hardware and software aspects, contributing to the understanding and advancement of autonomous vehicle perception technology (Fan, 2020). G. Chandan, A. Jain, H. Jain, and Mohana (2018) focused on real-time object detection and tracking using deep learning and OpenCV (Chandan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Advancing defense capabilities through integration of electro-optical systems and computer vision technologies

Elri,

Ergüzen

2024

JCEEES

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This paper presents a comprehensive study on the integration of cutting-edge technologies to enhance defense capabilities, focusing on object detection, tracking, and distance measurement tasks. Also, the integration of IMX219-77 cameras and Nvidia Jetson Nano for object detection, tracking, and distance measurement in electro-optical systems is proposed. The decision to combine these components was based on their individual strengths: the high resolution and wide field of view of the IMX219-77 cameras, and the low-power, high-performance capabilities of the Jetson Nano. Software-wise, OpenCV and GStreamer were chosen for their widespread use and capabilities in computer vision and multimedia processing tasks, respectively. The MOSSE algorithm was selected for object tracking due to its speed, efficiency, and resilience to lighting changes. Additionally, Depth Estimation (Stereo Vision) was employed for distance measurement by analyzing images captured from a pair of cameras. The proposed algorithm involves transmitting images from the cameras to the Jetson Nano, where OpenCV and GStreamer are utilized to process the data. The MOSSE algorithm is employed for object detection and tracking, while Depth Estimation generates depth maps to measure distances between objects and the camera. The study's results demonstrate accurate distance measurements with a margin of error ranging from 0 to 2 mm, falling below the acceptable threshold mentioned in academic literature. This aligns with similar studies in the field, showcasing the efficacy of the proposed integration for object detection, tracking, and distance measurement in electro-optical systems.

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Computer Stereo Vision for Autonomous Driving

Cited by 5 publications

References 76 publications

Radar Voxel Fusion for 3D Object Detection

Radar Voxel Fusion for 3D Object Detection

Composition and Application of Current Advanced Driving Assistance System: A Review

Advancing defense capabilities through integration of electro-optical systems and computer vision technologies

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