Comparative Analysis of Radar and Lidar Technologies for Automotive Applications

Bilik, Igal

doi:10.1109/mits.2022.3162886

Cited by 40 publications

(16 citation statements)

References 251 publications

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“…A variety of sensor systems can be used for this purpose, which differ in their robustness, accuracy and price. While LIDAR systems and cameras offer a high angular resolution [1], they provide poor robustness against weather conditions such as snow, fog and blinding sun [2]. Radar systems, on the other hand, have limited angular resolution due to their reduced aperture size in relation to the wavelength, but are able to detect targets with consistent precision almost regardless of weather conditions, making radar sensors a robust perception system [3], [4].…”

confidence: 99%

Probabilistic SAR Processing for High-Resolution Mapping Using Millimeter-Wave Radar Sensors

Grebner¹,

Grathwohl²,

Schoeder³

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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In the field of autonomous driving, highly accurate representations of the environment are essential for trajectory planning as well as for estimating the vehicle's location. Today, this can be achieved with the help of chirp-sequence radar sensors or radar sensor networks. The possibilities for environmental mapping cover simple point clouds, target list based grid maps and raw data based high resolution synthetic aperture radar (SAR) maps. While for target list based grid maps it has already been shown that a probabilistic occupancy grid map has significant advantages over an amplitude-based grid map in terms of robustness and resolution, no probabilistic approaches to SAR processing exist up to now. This paper presents a fundamental approach of processing radar raw data to generate high-resolution SAR images based on probabilities. A probabilistic SAR processing is presented which combines high resolution environmental mapping with amplitude independent target detection. Based on measurements, a qualitative and quantitative comparison between conventional amplitude and phase-based SAR processing, the presented probabilistic SAR processing, and a probabilistic target list-based occupancy grid map is performed. Since the presented algorithm is not limited to the automotive field and chirp-sequence radar sensors, it can be extended to arbitrary SAR applications and radar architectures.

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confidence: 99%

Probabilistic SAR Processing for High-Resolution Mapping Using Millimeter-Wave Radar Sensors

Grebner¹,

Grathwohl²,

Schoeder³

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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“…Given the increasing prominence of radar as a robust, cost-effective, and dependable perception sensor [5,9,30], there is a distinct need to undertake a comprehensive review of deep learning approaches specifically tailored toward radar-based SLAM methodologies.…”

Section: Introductionmentioning

confidence: 99%

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Brune,

Meisen,

Pomp

2024

Applied Sciences

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This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.

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“…Car manufacturers are installing ADAS in modern cars to enhance driver safety and comfort, as shown in Figure 1 . ADAS acquires the vehicle’s surrounding information from the environmental perception sensors [ 1 ]. Light detection and ranging (LiDAR), radio detection and ranging (RADAR), cameras, and ultrasonics are the current sensing technologies used for ADAS [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of MEMS-Based Automotive LiDAR Sensor and Its Simulation Model as per ASTM E3125-17 Standard

Haider

Cho

Pigniczki

et al. 2023

Sensors

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Measurement performance evaluation of real and virtual automotive light detection and ranging (LiDAR) sensors is an active area of research. However, no commonly accepted automotive standards, metrics, or criteria exist to evaluate their measurement performance. ASTM International released the ASTM E3125-17 standard for the operational performance evaluation of 3D imaging systems commonly referred to as terrestrial laser scanners (TLS). This standard defines the specifications and static test procedures to evaluate the 3D imaging and point-to-point distance measurement performance of TLS. In this work, we have assessed the 3D imaging and point-to-point distance estimation performance of a commercial micro-electro-mechanical system (MEMS)-based automotive LiDAR sensor and its simulation model according to the test procedures defined in this standard. The static tests were performed in a laboratory environment. In addition, a subset of static tests was also performed at the proving ground in natural environmental conditions to determine the 3D imaging and point-to-point distance measurement performance of the real LiDAR sensor. In addition, real scenarios and environmental conditions were replicated in the virtual environment of a commercial software to verify the LiDAR model’s working performance. The evaluation results show that the LiDAR sensor and its simulation model under analysis pass all the tests specified in the ASTM E3125-17 standard. This standard helps to understand whether sensor measurement errors are due to internal or external influences. We have also shown that the 3D imaging and point-to-point distance estimation performance of LiDAR sensors significantly impacts the working performance of the object recognition algorithm. That is why this standard can be beneficial in validating automotive real and virtual LiDAR sensors, at least in the early stage of development. Furthermore, the simulation and real measurements show good agreement on the point cloud and object recognition levels.

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Comparative Analysis of Radar and Lidar Technologies for Automotive Applications

Cited by 40 publications

References 251 publications

Probabilistic SAR Processing for High-Resolution Mapping Using Millimeter-Wave Radar Sensors

Probabilistic SAR Processing for High-Resolution Mapping Using Millimeter-Wave Radar Sensors

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Performance Evaluation of MEMS-Based Automotive LiDAR Sensor and Its Simulation Model as per ASTM E3125-17 Standard

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