MRPT: Millimeter-Wave Radar-Based Pedestrian Trajectory Tracking for Autonomous Urban Driving

Zhang, Zhenyuan; Wang, Xiaojie; Huang, Darong; Fang, Xiaoli; Zhou, Mu; Zhang, Ying

doi:10.1109/tim.2021.3139658

Cited by 37 publications

(6 citation statements)

References 53 publications

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“…Further, in this scenario it is more important to establish a robust track than to initialize tracks faster (which would be the more general case). In prior work (Mandischer et al, 2019), we have indicated that radar has no impairments by smoke or dust (see also Fang et al, 2022;Zhang et al, 2022), therefore, we assume that the results are similar to the firefighting use-case. However, future tests will need to support this assumption.…”

Section: Discussionmentioning

confidence: 85%

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Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

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Leg tracking is an established field in mobile robotics and machine vision in general.These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail.Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state-of-the-art performance, and that laser and fusion tracker outperform recent methods.

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Section: Discussionmentioning

confidence: 85%

“…They emphasize the usage of radar in degrading conditions, particularly in rain. Zhang et al (2022) propose a sequential Monte Carlo‐based track‐before‐detection method and analyze the effects in similar conditions as in Fang et al (2022). Both publications confirm that radar is not affected by degrading environmental conditions.…”

Section: Related Workmentioning

confidence: 99%

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Mandischer

Hou

Corves

2023

Journal of Field Robotics

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“…Despite the remarkable advances in image processing, it is very challenging to accurately map video data from different cameras into a unified three‐dimensional (3D) space for seamless traffic monitoring. Multiple millimeter‐wave radars can cooperatively sense and track traffic agents within a specific road area, whereas the low‐resolution data captured by radars make them challenging to identify static or small targets (e.g., pedestrians) (Zhang et al., 2021). Compared with cameras and radars, Lidar sensors can sense objects in a fully 3D manner and obtain high‐resolution point clouds.…”

Section: Introductionmentioning

confidence: 99%

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

Ma,

Zheng,

Wang

et al. 2024

Computer aided Civil Eng

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Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real‐life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual‐real‐fusion simulation (VRFS) framework that combines traffic simulation and point clouds of real‐world road environment to optimize RSUs’ deployment. Point clouds and triangular meshes are used to model static and dynamic obstacles, respectively. A structure‐retained spherical projection method is developed to efficiently emulate RSUs’ data collection. Based on the developed VRFS, the probabilistic occupancy maps (POM) are created to represent traffic scenarios. The POM‐based cross entropy (CE) is proposed as the surrogate metric for evaluating the detection performance of cooperative RSUs. The Bayesian optimizer is applied to optimize the RSUs’ placement parameters (decision variables) by minimizing CE. Test results show that it is viable to use the POM‐based CE as a proxy for evaluating cooperative RSUs’ sensing performance. Considering the occlusion effect adds to the efficacy of POM‐based CE as a surrogate metric. Compared with traffic volume, the adverse effect of the proportion of large vehicles on RSUs’ detection performance is more significant. There are no significant patterns regarding how the optimized RSU positions vary with traffic parameters. The comparisons with existing methods further verify the importance of considering both static and dynamic occlusions in optimizing RSUs’ placement. Besides, the proposed method can yield better optimization results more efficiently than existing approaches.

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“…Radar systems are employed in automotive applications for several purposes including collision avoidance (Moldovan et al., 2004), lane change assistance (Peng et al., 2022), pedestrian detection (Zhang et al., 2022), and adaptive cruise control (Zhang et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

AI‐Assisted Design of Printed Edge‐Fed Non‐Uniform Zig‐Zag Antenna for mm‐Wave Automotive Radar

Poli,

Rocca,

Rosatti

et al. 2024

Radio Science

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In this paper, the design of a novel horizontally polarized single‐layer antenna for 77 (GHz) automotive radar applications is4 addressed. An innovative non‐uniform zig‐zag parametrization of the antenna layout is considered to enable a more flexible control on both the impedance matching in the working frequency band and the shaping of the radiated beam pattern with respect to a standard (uniform) one without compromising the linear (horizontal) polarization of the radiated field. Such a polarization guarantees a lower back‐scattering from road pavements, resulting in a reduced amount of clutter and thus allowing a more robust target detection. Moreover, the single‐layer layout has several advantages in terms of fabrication simplicity/costs and mechanical robustness to vibrations. The design of the proposed non‐uniform zig‐zag antenna (NZA) is performed through a customized implementation of the System‐by‐Design (SbD) approach that fruitfully combines machine learning and evolutionary optimization to efficiently deal with the computational complexity at hand. An extensive numerical validation, dealing with designs of different lengths, verifies the high performance of the NZA in terms of beam direction deviation (e.g., BDD < 1 (deg)), sidelobe level (e.g., SLL < −18.2 (dB)), and polarization ratio (e.g., PR > 20 (dB)) within the working frequency band (GHz), as well as its superiority over competitive designs. Finally, the realization of a prototype and its experimental test, validate the proposed NZA concept for automotive mm‐wave radar applications in advanced driver assistance systems and autonomous vehicles such as, for instance, adaptive cruise control, collision avoidance, and blind spot detection.

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MRPT: Millimeter-Wave Radar-Based Pedestrian Trajectory Tracking for Autonomous Urban Driving

Cited by 37 publications

References 53 publications

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

AI‐Assisted Design of Printed Edge‐Fed Non‐Uniform Zig‐Zag Antenna for mm‐Wave Automotive Radar

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