Search Results (36)

Autonomous vehicles rely on complex sensing systems to perceive their environment and ensure safe operation. This review analyses the main sensor technologies used in self-driving vehicles, including cameras, LiDAR, radar, ultrasonic sensors and GNSS/IMU-based localisation systems. A core set of 40 primary research articles was systematically analysed to compare the capabilities, limitations and integration challenges of sensing technologies used in autonomous vehicles. In addition to these primary studies, further references were included to provide background information and describe emerging developments in autonomous sensing systems. The review shows that no single sensor technology can provide reliable perception under all environmental conditions. Camera systems offer rich visual information but are sensitive to lighting and weather conditions, while LiDAR provides highly accurate three-dimensional geometry but suffers from signal attenuation in rain and fog. Radar sensors demonstrate superior robustness in adverse weather and enable direct velocity measurement, although their spatial resolution remains limited compared to optical sensors. As a result, modern autonomous vehicles rely on multi-sensor fusion architectures that combine complementary sensing modalities to improve reliability and safety. The analysis also identifies several key research gaps in the current literature. In particular, there is a lack of systematic evaluation of trade-offs between sensor performance, computational requirements and vehicle energy consumption. Furthermore, the safety certification of artificial intelligence-based perception systems and the integration of emerging technologies such as FMCW LiDAR and terahertz radar remain open research challenges. Overall, the results suggest that the future of autonomous vehicle perception will depend not only on improvements in individual sensors but also on robust sensor fusion architectures, safety-certified AI models and energy-efficient sensor processing platforms. These findings provide guidance for researchers and engineers developing next-generation sensing systems for autonomous driving. Full article

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) based on coherent ranging is a technology capable of high-resolution distance measurement while remaining robust against ambient light interference. However, extending the measurable range remains challenging due to (i) the coherence length limitation of the laser and (ii) distance ambiguity caused by frequency ambiguity in coherent detection. To overcome these limitations, we propose an unambiguous range-extended FMCW LiDAR enabled by arrayed waveguide grating (AWG)-based spectral multiplexing. By spectrally demultiplexing the reference arm into four wavelength channels with sequentially designed optical path delays, multiple independent interference signals are obtained simultaneously without increasing the number of photodetectors or optical couplers. A channel-pair-based distance decoding algorithm is further introduced to resolve distance ambiguity by classifying detection outcomes across adjacent channels and selectively applying predefined operations. The proposed FMCW LiDAR system effectively extends the measurable range to approximately five times that of a conventional FMCW LiDAR. Experimental results demonstrate high measurement accuracy and successful reconstruction of three-dimensional distance maps, validating the system’s potential for extended-range FMCW LiDAR applications. Full article

Frequency Modulated Continuous Wave (FMCW) lidar exhibits excellent anti-interference capabilities and efficient range and velocity measurement performance. Operating range is a crucial metric for FMCW lidar systems. To address the limitations of traditional radar equations, which do not account for optical mixing and balanced detection, a FMCW lidar equation based on optical mixing has been derived. This equation incorporates parameters such as the FMCW lidar system, average noise power, signal-to-noise ratio, and coherent detection gain, while also considering the relationship between atmospheric transmittance and operating range. Finally, an experimental FMCW lidar system with a wavelength of 1550 nm was constructed to test the operating range for water surface tar-gets. The experimentally measured average maximum range was 272.49 m, while the maximum operating range calculated using the derived lidar equation was 293.60 m, with a relative error of 7.74%, thereby validating the accuracy of the equation. This equation can be applied to evaluate the maximum operating range of FMCW lidar in various environments, such as autonomous driving and low-altitude surveillance, providing theoretical support for the design and performance assessment of FMCW lidar systems. Full article

To address the accuracy calibration issue of the high-precision FMCW laser scanning measurement system for the large-aperture antenna of the Fengyun-4 microwave sounding satellite in orbit, this paper proposes a system calibration method based on space ranging correction. First, by analyzing the geometric structure and optical axis offset errors of the FMCW measurement system, a comprehensive error model comprising 13 key parameters was established. Second, a calibration field was constructed using a high-precision reference scale and planar targets. The spatial ranging correction method was employed to eliminate reliance on the accuracy of reference point coordinates inherent in traditional approaches, and nonlinear least-squares optimization was used to estimate the error parameters. Finally, a calibration scheme involving four operational conditions was implemented, with validation performed under three independent operational conditions. Experimental results show that the RMS error in relative distance between two points decreased from 17.5 mm to 2.3 mm after calibration. The ICP registration residual for the spatial point cloud was reduced to 2.5 mm, and point cloud shape fidelity improved by 86.6%. This validates the effectiveness and generalization capability of the proposed method. This research provides a reliable technical approach for spatial 3D calibration of lidar systems. Full article

Frequency-Modulated Continuous Wave (FMCW) lidar for long-distance measurements face challenges in signal acquisition and frequency estimation due to the high sampling rates required, leading to increased processing load, cost, and power consumption. Although sub-Nyquist sampling can alleviate the burden of high sampling rates, it requires a complex reconstruction process that degrades real-time performance. In this study, we propose a frequency estimation algorithm based on multi-coset sampling (MCS) that not only reduces the sampling rate but also avoids reconstructing the original signal. This algorithm performs a preliminary frequency estimation by exploiting the relationship among the signal support set, the measured sequences by sampling spectrum, and the original signal spectrum, and then refines the spectrum to obtain an accurate frequency estimate. Since the algorithm relies solely on the sampled sequences for estimation, frequency ambiguity may occur during the calculation. We analyze the causes of ambiguity and propose a support set determination method to eliminate this issue. Simulation results demonstrate that the proposed algorithm attains the Cramér–Rao lower bound (CRLB) at low signal-to-noise ratios. It achieves a 10-fold improvement over Nyquist method and a 35 dB SNR reduction compared with the original MCS, while maintaining stable performance down to −20 dB. Full article

Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) is a high-precision ranging and imaging system that has been widely used in various areas, such as self-driving vehicles and industrial inspection. However, during detection, the system is susceptible to noise interference. This interference results in a decrease in the signal-to-noise ratio (SNR) of mixed signals, which affects the ranging accuracy. In this study, a MATLAB r2021b simulation is used to generate LiDAR transmitted and echo signals, and Gaussian noise is introduced. After mixing, empirical mode decomposition (EMD) and wavelet transform (WT) are used to denoise mixed signals, and the denoising effects of different wavelet basis functions under different SNRs are analysed. Furthermore, an experimental FMCW LiDAR system is set up to collect practical target echo signals, and the simulation results are validated through experiments under various illumination conditions. The results also show that the noise in FMCW LiDAR signals is dominated by Gaussian noise and that the influence of environmental noise is minimal. The combined EMD-WT denoising algorithm and its wavelet basis optimisation strategy proposed in this study can be directly applied to practical scenarios with strict requirements for FMCW LiDAR signal quality, such as autonomous driving, aircraft navigation, and precision industrial measurement, providing theoretical basis and experimental support for wavelet basis selection and denoising strategies in different noise environments. Full article

Small and medium Unmanned Aerial Vehicles (UAVs) are commonly equipped with diverse sensors for situational awareness, including cameras, Frequency-Modulated Continuous-Wave (FMCW) radars, Light Detection and Ranging (LiDAR) systems, and ultrasonic sensors. However, optical systems are constrained by adverse weather and darkness, while the limited detection range of compact FMCW radars-typically a few hundred meters-is often insufficient for higher-speed UAVs, particularly those operating Beyond Visual Line of Sight (BVLOS). This paper presents a Collision Avoidance System (CAS) based on a lightweight pulse radar, targeting medium UAV platforms (10–300 kg MTOM) where installing large, nose-mounted radars is impractical. The system is designed for obstacle detection at ranges of 1–3 km, directly addressing the standoff distance limitations of conventional sensors. Beyond its primary sensing function, the pulse architecture offers several operational advantages. Its lower time-averaged power also results in a reduced electromagnetic footprint, mitigating interference and supporting emission-control objectives. Furthermore, pulse radar offers greater robustness against interference in dense electromagnetic environments and lower power consumption, both of which directly enhance UAV operational endurance. Field tests demonstrated a one-to-one correspondence between visually identified objects and radar detections across 1–3 km, with ${\mathrm{P}}_{\mathrm{FA}}$ = 1.5%, confirming adequate standoff for tens of seconds of maneuvering time, with range resolution of 3.75 m and average system power below 80 W. Full article

Ship berthing operations are inherently challenging for maritime vessels, particularly within restricted port areas and under unfavorable weather conditions. Contrary to autonomous open-sea navigation, autonomous ship berthing remains a significant technological challenge for the maritime industry. Lidar and optical camera systems have been deployed as auxiliary tools to support informed berthing decisions; however, these sensing modalities are severely affected by weather and light conditions, respectively, while cameras in particular are inherently incapable of providing direct range measurements. In this paper, we introduce a comprehensive, Radar-Based Berthing-Aid Dataset (R-BAD), aimed to cultivate the development of safe berthing systems onboard ships. The proposed R-BAD dataset includes a large collection of Frequency-Modulated Continuous Wave (FMCW) radar data in point cloud format alongside timestamped and synced video footage. There are more than 69 h of recorded ship operations, and the dataset is freely accessible to the interested reader(s). We also propose an onboard support system for radar-aided vessel docking, which enables obstacle detection, clustering, tracking and classification during ferry berthing maneuvers. The proposed dataset covers all docking/undocking scenarios (arrivals, departures, port idle, and cruising operations) and was used to train various machine/deep learning models of substantial performance, showcasing its validity for further autonomous navigation systems development. The berthing-aid system is tested in real-world conditions onboard an operational Ro-Ro/Passenger Ship and demonstrated superior, weather-resilient, repeatable and robust performance in detection, tracking and classification tasks, demonstrating its technology readiness for integration into future autonomous berthing-aid systems. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems are extensively utilized in industrial metrology, autonomous navigation, and geospatial sensing due to their high precision and resilience to interference. However, the intrinsic nonlinear dynamics of laser systems introduce significant distortion, adversely affecting measurement accuracy. Although conventional iterative pre-distortion correction methods can effectively mitigate nonlinearities, their long-term reliability is compromised by factors such as temperature-induced drift and component aging, necessitating periodic recalibration. In light of recent advances in artificial intelligence, deep reinforcement learning (DRL) has emerged as a promising approach to adaptive nonlinear compensation. By continuously interacting with the environment, DRL agents can dynamically modify correction strategies to accommodate evolving system behaviors. Nonetheless, existing DRL-based methods often exhibit limited adaptability in rapidly changing nonlinear contexts and are constrained by inefficient uniform experience replay mechanisms that fail to emphasize critical learning samples. To address these limitations, this study proposes an enhanced Soft Actor-Critic (SAC) algorithm incorporating a hybrid prioritized experience replay framework. The prioritization mechanism integrates modulation frequency (MF) error and temporal difference (TD) error, enabling the algorithm to dynamically reconcile short-term nonlinear perturbations with long-term optimization goals. Furthermore, a time-varying delayed experience (TDE) injection strategy is introduced, which adaptively modulates data storage intervals based on the rate of change in modulation frequency error, thereby improving data relevance, enhancing sample diversity, and increasing training efficiency. Experimental validation demonstrates that the proposed method achieves superior convergence speed and stability in nonlinear correction tasks for FMCW LiDAR systems. The residual nonlinearity of the upward and downward frequency sweeps was reduced to $1.869\times {10}^{-5}$ and $1.9411\times {10}^{-5}$, respectively, with a spatial resolution of 0.0203m. These results underscore the effectiveness of the proposed approach in advancing intelligent calibration methodologies for LiDAR systems and highlight its potential for broad application in high-precision measurement domains. Full article

Frequency-Modulated Continuous-Wave (FMCW) Laser Detection and Ranging (LiDAR) systems are widely used due to their high accuracy and resolution. Nevertheless, conventional distance extraction methods often lack robustness in noisy and complex environments. To address this limitation, we propose a deep learning-based signal extraction framework that integrates a Dual Convolutional Neural Network (DCNN) with a Transformer model. The DCNN extracts multi-scale spatial features through multi-layer and pointwise convolutions, while the Transformer employs a self-attention mechanism to capture global temporal dependencies of the beat-frequency signals. The proposed DCNN–Transformer network is evaluated through beat-frequency signal inversion experiments across distances ranging from 3 m to 40 m. The experimental results show that the method achieves a mean absolute error (MAE) of 4.1 mm and a root-mean-square error (RMSE) of 3.08 mm. These results demonstrate that the proposed approach provides stable and accurate predictions, with strong generalization ability and robustness for FMCW LiDAR systems. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems frequently experience noise interference during multi-target measurements in real-world applications, resulting in target overlapping and diminished detection accuracy. Conventional denoising approaches—such as Empirical Mode Decomposition (EMD) and wavelet thresholding—are often constrained by challenges like mode mixing and the attenuation of weak target signals, which limits their detection precision. To address these limitations, this study presents a novel denoising framework that integrates an optimized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and singular value decomposition (SVD). The CEEMDAN algorithm’s two critical parameters—the noise standard deviation and the number of noise additions—are optimally determined using particle swarm optimization (PSO), with the envelope entropy of the intrinsic mode functions (IMFs) serving as the fitness criterion. IMFs are subsequently selected based on spectral and amplitude comparisons with the original signal to facilitate initial signal reconstruction. Following CEEMDAN-based decomposition, SVD is employed with a normalized soft thresholding technique to further suppress residual noise. Validation using both synthetic and experimental datasets demonstrates the superior performance of the proposed approach over existing methods in multi-target scenarios. Specifically, it reduces the root mean square error (RMSE) by 45% to 59% and the mean square error (MSE) by 34% to 69%, and improves the signal-to-noise ratio (SNR) by 1.85–4.38 dB and the peak signal-to-noise ratio (PSNR) by 1.18–6.94 dB. These results affirm the method’s effectiveness in enhancing signal quality and target distinction in noisy FMCW LiDAR measurements. Full article

Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. Integrating LiDARs into electronic and photonic semiconductor chips can lower their cost, size, and power consumption, making them affordable for cost-sensitive applications. Additionally, simple designs are required, such as FMCW signal generation by the direct modulation of the current of a semiconductor laser. However, semiconductor lasers are inherently nonlinear, and the driving waveform needs to be optimized to generate linear FMCW signals. In this paper, we employ pre-distortion techniques to compensate for chirp nonlinearity, achieving frequency nonlinearities of 0.0029% for the down-ramp and the up-ramp at 55 kHz. Experimental results demonstrate a highly accurate LiDAR system with a resolution of under 5 cm, operating over a 210-m range through single-mode fiber, which corresponds to approximately 308 m in free space, towards meeting the requirements for long-range autonomous driving. Full article

Frequency tuning nonlinearities in semiconductor lasers constitute a critical factor that degrades measurement precision and spectral resolution in frequency-modulated continuous-wave (FMCW) LiDAR systems. This study systematically investigates the influence of nonlinear beat signal phase distortions on spectral peak broadening and develops a phase-fitting-based pre-correction algorithm. To further enhance system performance, an electro-optic phase-locked loop architecture combined with non-uniform discrete Fourier transform signal processing is implemented, establishing a comprehensive solution for tuning nonlinearity suppression. Experimental validation demonstrates a sub-18 µm standard deviation in absolute distance measurements at a 19 m target range. This integrated approach represents a significant advancement in coherent frequency-sweep detection methodologies, offering considerable potential for high-precision photonic radar applications. Full article

For Advanced Driver Assistance Systems (ADASs), lots of researchers have been constantly researching various devices that can become the eyes of a vehicle. Currently represented devices are LiDAR, camera, and radar. This paper suggests one of the operation processes to study radar, which can be used regardless of climate change or weather, day or night. Thus, we propose a simple and easy calibration method for Multi-Input Multi-Output (MIMO) radar to guarantee performance with initial calibration parameters. Based on a covariance matrix, the modified signals of all channels improve performance, reducing unexpected interferences. Therefore, using the proposed coupling matrix, we can reduce unexpected interference and generate accurately calibrated results. To prove and verify the improvement in our method, a practical experiment is conducted with Frequency-Modulated Continuous-Wave (FMCW) MIMO radar, mounted on an automotive. Full article

Frequency sweeping linearity is essential for Frequency-Modulated Continuous Wave (FMCW) Light Detection and Ranging (LIDAR), as it impacts the ranging resolution and accuracy of the system. Pre-distortion methods can correct for frequency sweeping nonlinearity; however, residual minor nonlinearities can still degrade the system ranging resolution, especially at far distances. Therefore, the precise measurement of minor nonlinearities is particularly essential for long-range FMCW LIDAR. This paper proposes a calibration-free MZI for measuring optical frequency sweeping nonlinearity, which involves alternately inserting two short polarization-maintaining fibers with different delays into one arm of an MZI, and after two rounds of beat collection, the optical frequency sweep curve of the light source is accurately measured for nonlinearity evaluation. Using the proposed method, the nonlinearity of a frequency-swept laser source is measured to be 0.2113%, and the relative nonlinearity is 5.3560 × 10⁻⁵. With the measured frequency sweep curve, we simulate the beat signal and compare it with the collected beat signal in time and frequency domain, to verify the accuracy of the proposed method. A test conducted at 24.1 °C, 30.4 °C, 39.5 °C and 44.0 °C demonstrate the method’s insensitivity to temperature fluctuations. Based on the proposed MZI, a tunable laser is pre-distorted and then used as light source of a FMCW lidar. A wall at 45 m and a building at 1.2 km are ranged by the lidar respectively. Before and after laser pre-distortion, the FWHM of echo beat spectrum are 25.635 kHz and 9.736 kHz for 45 m, 747.880 kHz and 22.012 kHz for 1.2 km. Full article