Synthetic aperture radar (SAR) raw data simulators (RDSs) play a critical role in system design, mission planning, and algorithm evaluation, particularly for emerging imaging modes such as frequency-scanning (F-SCAN) SAR. However, existing simulators are often either computationally inefficient or unsuitable for this mode. To address these limitations, a dedicated RDS for F-SCAN SAR is first proposed, featuring detailed mathematical derivation, explicit modeling of antenna pattern effects, and a computational complexity analysis. Simulation experiments validate the proposed approach, demonstrating an efficiency improvement of 97.4% compared with the conventional time-domain method, while maintaining root-mean-square amplitude and phase errors on the order of 10-3. These results confirm that the proposed simulator is both efficient and accurate, providing a practical tool for F-SCAN SAR research.

Time synchronization and phase synchronization are indispensable for spaceborne bi-/multi-static SAR systems. The LuTan-1 (LT-1) bistatic SAR system employs a time synchronization scheme combining pulse-per-second (PPS) signals with GNSS-disciplined oscillators (GDOs), along with a novel non-interrupted alternate pulse (NIAP) scheme for phase synchronization. Furthermore, the time synchronization accuracy can be further enhanced by applying the two-way time transfer (TWTT) technique based on synchronization data from the NIAP scheme. To accurately evaluate the synchronization performance of the LT-1 system during in-orbit operation, an assessment method based on the two-dimensional (2D) positional offset of calibration targets is proposed. The method compares the theoretical offsets derived from imaging geometry with the measured offsets extracted from monostatic and bistatic SAR images to determine the system’s time and frequency deviations. Using 16 repeat-pass acquisitions over the calibration field in Hami, Xinjiang, during LT-1’s bistatic operation phase, the in-orbit synchronization performance of LT-1 is demonstrated. The PPS+GDO scheme achieves time synchronization accuracy under 100 ns, while the TWTT algorithm enhances this metric by three orders of magnitude, which is better than 0.10 ns. After NIAP phase synchronization, the LT-1 system exhibits a frequency deviation of approximately −2.17 × 10−3Hz, corresponding to frequency stability of 2 × 10−12.

Scattering properties of lunar surface rocks are important guides for remote sensing observation of the lunar surface and discrimination of geologic features. Previous studies primarily focused on the scattering effects of surface or partially buried rocks in terms of angle or frequency, with limited research addressing SAR simulation images of rocks on the lunar surface. This study proposes a three-dimensional (3D) scattering model that integrates the vector radiative transfer (VRT) method with lunar surface rock scattering mechanisms while effectively combining topographic data, rock abundance data, and radar parameters. To implement this model, an end-to-end computational framework is developed to process input datasets and apply the 3D scattering model for backscattering coefficient simulation, reducing uncertainties in the input parameters. Several case studies are performed on Mini-RF observational data. Simulation results demonstrate that the proposed approach shows good consistency with radar data, particularly in terms of distribution characteristics and average backscattering coefficient errors, with an error margin of less than 3 dB.

The spaceborne multistatic synthetic aperture radar (SAR) system offers a flexible baseline that provides more observation angles and higher interferometry accuracy. However, any phase deviation among the independent oscillators in the spaceborne multistatic SAR system can cause a residual modulation of the echoes. Therefore, accurate phase synchronization is crucial for the system. The pulsed alternate synchronization scheme accurately extracts phase errors between different platforms, as verified in the TanDEM-X mission. Furthermore, an advanced noninterrupted pulsed alternate scheme uses the time interval of transmitting sequence to realize phase synchronization without interrupting the normal operation of the radar, which is verified in the LuTan-1 mission. However, with the increasing number of spaceborne multistatic SAR platforms, the time interval of the system may not be sufficient to support noninterrupted phase synchronization. To this end, this article proposes a novel phase synchronization method to improve the efficiency of phase synchronization for spaceborne multistatic SAR systems. First, a model for phase synchronization is built based on the pulsed alternate synchronization scheme, and the constraint between phase synchronization accuracy and waveform properties is analyzed in detail. Second, a quasi-orthogonal waveform optimization method, which can realize the rapid generation of phase synchronization waveform with better correlation properties, is introduced to improve the accuracy of phase synchronization. Third, to further reduce cross correlation energy between waveforms, we introduce generalized short-term shift-orthogonal (STSO) waveforms for phase synchronization. This waveform can achieve local orthogonality with a known baseline, improving the accuracy of phase synchronization greatly. Finally, the proposed method is verified through detailed simulations and ground experiments.

Most of existing steering system control strategies pay little attention to return time and speed. Additionally, most algorithms do not consider the impact of the applied torque of the driver on the steering wheel when determining the optimal return speed while the wheel is being held. This highlights an ongoing need to improve the return-to-center quality in vehicles. To address this challenge, this study proposes a steering motion quality control method based on a motion-closed loop. Here, we delineate the context wherein, by establishing a desired returnability, the return-to-center speed during release or holding phases is achieved via direct control through a motion closed-loop. This approach harmonizes the steering feel with the return-to-center performance, thereby enhancing the stability of the vehicle during straight-line driving. Subsequently, we validate the efficacy and robustness of the proposed steering motion quality-oriented vehicle return-to-center control strategy in managing returnability, as evidenced by low-speed and high-speed return-to-center simulation tests using a driving simulator.

The registration and mapping of The Miniature Radio Frequency (Mini-RF) images from The Lunar Reconnaissance Orbiter (LRO) and the derived data products has been a problem for lunar remote sensing analysis and multi-source data fusion. In this context, we propose an automated registration methodology for Mini-RF S-band level-1 data. This method corrects offsets from synthetic aperture radar (SAR) imaging to match SAR data with optical and DEM data in the map-projection. It could produce maps in polar and non-polar area with precision comparable to manually registered maps. Using manually-labeled craters features for evaluation, the processed maps match well to LRO Wide Angle Camera (WAC) and Digital Elevation Model (DEM) data on 100m- level.

Multiaperture echo signals collected by distributed array SAR can be reconstructed to realize High-Resolution and Wide-Swath (HRWS) imaging. To achieve the HRWS imaging of distributed SAR, an advanced Azimuth Multi-Aperture Reconstruction (AMAR) method is put forward for the first time. First, to eliminate the trajectory offset of distributed SAR, the first-order baseline deviation, second-order baseline deviation and azimuth space variation error are compensated in turn for the calibrated and undersampled echo signals, and the echoes containing distorted azimuth ambiguity can be obtained. Second, the distortion of ambiguity is corrected by range decompression, anti-migration correction, etc. Finally, the echoes satisfying the Nyquist theorem are derived by the AMAR of Train formation, and the bistatic SAR imaging is performed. In addition, some simulations are conducted to evaluate the performance of the proposed method, and the results demonstrate that the method can achieve AMAR and HRWS imaging of distributed array SAR.

Azimuth ambiguity in synthetic aperture radar (SAR) images results from the limited azimuth sampling rate and seriously affects image quality and normal applications. Multiple algorithms for azimuth ambiguity elimination have been proposed. However, most algorithms adopt a single-channel model and ignore the effect of additional reconstruction operations on ambiguity in azimuth multichannel systems (MCSs). In this letter, the particularity of azimuth ambiguity for MCSs is analyzed theoretically. A refined ambiguity suppression scheme based on multichannel cancellation (MCC) and ambiguity refocusing is proposed. The ambiguity region is first located through the comparison of multi-look image pairs. Then the interference image for ambiguity extraction is cancellated through MCC. Consequently, the ambiguous image is refocused and extracted. Finally, the ambiguity is reversed to the echo domain to handle the special replicated ambiguity problem for MCSs. With the proposed scheme, ambiguity suppression performance has improved for MCSs. The validity of the proposed algorithm is further verified through LuTan-1 real data.

The echo separation issue of different transmit antennas is the most technical challenge in realizing multiple-input and multiple-output synthetic aperture radar (MIMO SAR) with same frequency band, especially for low-computing echo separation, making it extremely difficult towards the practical application for the spaceborne MIMO SAR. Based on the orthogonal frequency-division multiplexing (OFDM) chirp waveforms, this letter proposes an innovative echo separation scheme with digital beamforming (DBF) and bandpass filtering (BPF) on board and bandpass-null steering on the ground for the spaceborne MIMO SAR. This scheme transfers the complex computing process on board to the ground, thus significantly reduce the computational load and relieve the resource occupation on board. Also, the perfect separation of interested echoes from interference can be achieved by this scheme. Finally, performance comparisons and simulation results show the effectiveness of the proposed scheme. The proposed scheme enables a high-efficiency and great-performance echo separation for the spaceborne MIMO SAR and makes the MIMO SAR a more promising technique for future SAR missions.

The azimuth multichannel synthetic aperture radar (SAR), as one of the mainstream technologies for achieving high-resolution and wide-swath (HRWS) imaging, has been successfully employed in several on-orbit SAR missions. However, the unavoidable phase errors among channels result in azimuth ambiguity, deteriorating the recognizability of targets in SAR images. Additionally, the radio frequency interference (RFI) exacerbates the difficulty of phase error estimation. To address this issue, a phase error calibration method based on least spectrum difference (LSD) is proposed. Firstly, the multichannel signals are reconstructed using the linear mapping form of the reconstruction algorithm. Secondly, the objective function is established based on the continuity of the azimuth spectrum, by which only the signals near the discontinuity points are proposed. Finally, the optimal estimation of the phase differences can be obtained after iteration. In LSD method, the RFI to the objective function is mitigated due to the operation in the range-Doppler domain, and the iteration is proposed only using a few data near the discontinuity points thus saving much computational cost. Experimental results based on the simulated data and real bistatic echoes of the LuTan-1 (LT-1) mission validate the superiority of the proposed LSD method.

Range ambiguity is a technical challenge due to the deterioration of the image quality in the multistatic synthetic aperture radar (SAR) constellations. This article proposes an effective range ambiguity suppression scheme based on multiechoes coherent processing. First, a general signal model impacted by range ambiguity is built up based on the geometry of the multistatic SAR constellation, showing the different phase characteristics of the desired and ambiguous signals between the satellites. Then, an effective processing scheme for range ambiguity suppression is proposed based on the different phase characteristics, and the corresponding ambiguity suppression performance (ASP) is analyzed accordingly. This scheme can achieve a coherent summation of the desired signals by compensating for the corresponding phase difference, while the ambiguous signals are summed incoherently. Therefore, the range ambiguities can be suppressed without increasing the SAR instrument complexity. Finally, the system and imaging simulation results are provided to validate the theoretical analysis of the ASP and demonstrate the effectiveness of the proposed scheme, respectively.

Frequency scanning (F-SCAN) synthetic aperture radar (SAR), as an advantageous choice for SAR systems operating at higher carrier frequencies, can achieve high performance in terms of swath width, azimuth resolution, and signal-to-noise ratio (SNR). In this article, a novel nonlinear F-SCAN (NF-SCAN) SAR imaging mode is proposed. Compared to the F-SCAN SAR, NF-SCAN SAR has two main advantages: one is to change the distribution of the transmit bandwidth over the swath by precisely tuning the beam scanning response to frequency. The other is to adapt the SNR distribution by adjusting the beam scanning response to time. First, this article derives the system parameters for NF-SCAN SAR. Second, methods for designing nonlinear beam scanning responses (NBSRs) to frequency and time (NBSR-F and NBSR-T) are proposed, by which a well-balanced ground range resolution and adaptive SNR are acquired. Third, a waveform design method dedicated to NF-SCAN SAR is given and the corresponding signal model is derived in detail. To reduce the data rate of NF-SCAN SAR with an incompletely compressed echo window, an improved data subsampling algorithm (IDSSA) is proposed, where a precise filter is designed, thus avoiding the loss of effective bandwidth. Finally, simulation experiments are conducted to verify the superiority of NF-SCAN SAR imaging mode. The proposed NF-SCAN SAR can be viewed as an important candidate for high-performance spaceborne SAR.

The Bistatic SAR (BiSAR) employs the Alternating Transmitting Mode (ATM) to capture multi-dimensional scattering information of the target. However, the drawback of the ATM lies in doubling the Pulse Repetition Frequency (PRF) and reducing the echo window (i.e., swath width), consequently limiting the High-Resolution and Wide-Swath (HRWS) imaging capabilities of BiSAR. To achieve HRWS imaging, the paper proposes new insights into the ATM of bistatic multi-channel SAR. First, multi-channel signal models containing time and phase synchronization deviations are established for BiSAR. Then, the imaging mode, clock synchronization scheme, and imaging method to achieve HRWS imaging are detailed. Finally, a group of system parameters for the HRWS imaging of the ATM is designed based on the LuTan-1 (LT-1) BiSAR system, and the synchronization and imaging simulations of the ATM are conducted using the data of the LT-1. Simulation results demonstrate that the ATM can achieve the HRWS imaging mode of3m/60km. In short, these new insights pave the way for the HRWS imaging of distributed SAR.

Benefiting from the wide coverage and high resolution of synthetic aperture radar (SAR) data, interferometric SAR (InSAR) has significant advantages in wide-area deformation detection and monitoring. In order to improve computational efficiency and save computational resources, it is expected to perform the deformation area identification first as accurately as possible, and then perform local time series inversion for the specific deformation area. However, with the interference of tropospheric delay, especially its systematic component, the accurate identification of the deformation area becomes challenging. To address this issue, we propose a two-step tropospheric delay removal method including time domain correction and spatial domain correction. The time domain correction is used to avoid the effect of the systematic component of tropospheric delay so as to derive an accurate deformation rate map. The purpose of the spatial domain correction is to finely remove the effect of tropospheric delay in local area to recover the correct deformation time series. Applying our method, external data based method and existing classical SAR data based method to the reservoir area of the Lianghekou hydropower station with Sentinel-1A ascending data for comparison, the results demonstrate the advantages of our method in deformation area identification and deformation monitoring.

Convolutional neural networks (CNNs) have achieved greate success in a variety of computer vision tasks. However, they are susceptible to elaborate, human-imperceptible adversarial noise patterns, which limit their deployment in safety-critical systems. In this paper, we propose an adversarial training method for synthetic aperture radar (SAR) image segmentation, which can effectively suppress the effects of adversarial perturbations. The proposed method introduces a multiple intermediate domain mechanism to enhance the robustness of the network to adversarial attacks, by dynamically adjusting the distribution of input data during the training process, without modifying the network structure or adding a separate mechanism to detect adversarial images. Experiments validate that our approach not only improves the segmentation accuracy of the network, but also effectively enhances the robustness of the network when facing white-box adversarial attacks.

Phase synchronization plays a crucial role in the LuTan-1 (LT-1) bistatic synthetic aperture radar (BiSAR) system, as it aims to eliminate additional azimuthal phase modulation caused by oscillator differences. However, for the pulse alternating transmission system operating in the L-band, the presence of radio frequency interference (RFI) poses an inevitable challenge. Serious RFIs introduce phase jitters, compromising the accuracy of synchronization. In this letter, an effective method for detecting and removing phase jitters is proposed to enhance synchronization accuracy. In the proposed method, the jitter features are separated by the iteratively reweighted least squares (IRLS) in the instantaneous frequency domain based on the established synchronization phase model. Then the jitter positions are detected by correlated peaks between the designed convolutional kernels and jitters. Finally, a polynomial model is utilized to remove jitters and assist in phase unwrapping. The synchronization phases acquired by the LT-1 mission are used to verify the feasibility of the proposed algorithm. The improved imaging quality demonstrates the effectiveness of the proposed method and confirms its ability to ensure the high-precision generation of the LT-1 BiSAR images.

The distributed multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) system composed of numerous separated transmitters and receivers can realize multi-angle imaging, cross- and along-track interferometry through their high-precision cooperation. However, phase synchronization is a major factor affecting the cooperation of distributed MIMO-SAR. As such, this paper proposes a Link-Free Permanent-C (LFPC) phase synchronization scheme for the first time. The designed system framework, basic principle and error model of the scheme are described, and some simulations are performed to evaluate the synchronization accuracy of the LFPC scheme. The results demonstrate that the LFPC scheme has the frequency stability of 10-15when SNR ≥ 50 dB, and it is a candidate for the future distributed SAR mission.

The innovative bistatic synthetic aperture radar (BiSAR) mission LuTan-1 (LT-1) uses a noninterrupted synchronization scheme to achieve high-precision phase synchronization. While radio frequency interference (RFI) is a major factor in deteriorating phase synchronization performance. To present the effect of RFI on the synchronization phase clearly, the characteristics of RFI in the synchronization link are described in detail, and a precise analytic expression between the amplitude, frequency, and phase of RFI and synchronization phase error is established. Furthermore, a novel pulse-compression-based notch (PCN) method is proposed to eliminate the phase error introduced by RFI. In the proposed method, the saturated distortion signals resulting from strong interferences are detected and discarded by the distribution features of their modes. Then, inspired by contrary thinking, the synchronization signal after pulse compression is notched instead of RFI. A fast missing data iterative adaptive approach (Fast MIAA) is performed to recover the gaped RFI signal and remove it from the compressed signal. Finally, the correct synchronization phases can be extracted from the peak positions of the remaining signals. Experimental results derived from using simulated and real synchronization data of the LT-1 system validate the performance of the proposed RFI mitigation method.

The channel imbalance and crosstalks are the two major factors for the polarimetric calibration of the circular quad-polarimetric (CQP) synthetic aperture radar (SAR) systems. In existing methods using distributed targets, the latter is usually ignored, which may lead to unbearable errors in target classification, surface parameter inversion, and so on. To address this issue, this article proposes a modified iterative calibration method using distributed targets that satisfy quasi-azimuth symmetry to calibrate both the channel imbalance and crosstalks of the CQP SAR systems. First, a stable distributed target selection strategy is proposed based on the correlation coefficient of LL polarization and RR polarization, the cross- and co-polarization backscatter ratio, and the equivalent number of looks (ENL). These parameters are insensitive to polarization distortion, and their typical ranges are determined via numerical simulations. Their combination helps select the targets that satisfy the quasi-azimuth symmetry, which is critical for calibrating the crosstalks. Then, the calibration can be conducted using the selected target. Finally, the phase ambiguity of the receive channel imbalance ratio, commonly found in distributed-target-based algorithms, is eliminated using a dipole target. Through the calibration of Gaofen-3 data and the statistical analysis of residual distortion, the effectiveness of the proposed method is verified.

An advanced sparse multi-channel system is proposed for spaceborne digital beamforming synthetic aperture radar (DBF-SAR), which can suppress pulse extension loss (PEL) and frequency dispersion loss (FDL) without increasing the computational load and system complexity. First, conventional scan-on-receive (SCORE) technique is reviewed and a matching ratio (MR) is proposed to evaluate the mismatch between the formed beam pattern and the pulse signal amplitude. To mitigate the PEL and FDL, the novel sparse SCORE (S-SCORE) based on the optimization of the sparse channel distribution is proposed. The impact of sparse channel distribution is analyzed and the method to optimize the distribution based on the maximized MR is proposed accordingly. Finally, the results of simulations and experiments are provided to demonstrate the superiority of the proposed S-SCORE technique. The work in this paper can be seen as an important candidate for future spaceborne DBF-SAR.

Barrage jamming technique with controllable jamming coverage against synthetic aperture radar (SAR) systems is of great importance in electronic countermeasures. However, it is still a difficulty for the jammer to accurately impose controllable two-dimensional (2-D) local jamming on the regions of interest (ROIs). In this respect, a new parameterized and large-dynamic-range precise controllable (PLDR-PC) jamming method has been proposed in this paper to assist in solving such problems. Based on the SAR imaging properties of linear frequency modulation (LFM) case, the range and azimuth modulation factors have been well designed to generate large dynamic controllable coverage of jamming signals with high 2-D processing gain. In such a context, the PLDR-PC technique can provide the optimal power allocation and considerably reduce the jamming power while still ensuring the satisfactory performance. The proposed PLDR-PC technique can improve the jamming efficiency and considerably reduce the exposure probability of the jammer. Moreover, to improve the barrage jamming performance, the parameter estimation error model is also established to determine the simple yet valid jamming strategy in practical implementations. Finally, extensive numerical simulations in comparison with the current jamming methods have been carried out to demonstrate the effectiveness and prospect of the PLDR-PC technique against airborne/spaceborne SAR systems.

An accurate baseline of synthetic aperture radar (SAR) interferometry (InSAR) is an important parameter for the geodetic application of the InSAR data. Although some advanced SAR satellites have precise orbit determination, there are still many SAR satellites suffering from baseline inaccuracies, such as GF-3. In this article, an image-based estimator for baseline correction is proposed, which requires only the external digital elevation model (DEM) data. The idea of the method is to project the orbit error phase onto the phase components carrying the baseline error information, which is called orbit error phase bases in this article, and to correct the baseline according to the projection coefficients. Since the pure orbit error phase is unavailable, the residual phase of the interferogram is used to approximate the orbit error phase, and a series of processes are introduced to weaken the effect of this approximation. Both the simulated and real data from GF-3 SAR are used to validate the proposed method, and a comparison with the conventional nonlinear least-square and the latest proposed flat-Earth phase-based baseline refinement methods are made. The results indicated the superior accuracy and robustness of our method, especially in areas with higher relief and wider coverage.

Robust and efficient vehicle detection is an important task of environment perception of intelligent vehicles, which directly affects the behavior decision-making and motion planning of intelligent vehicles. Due to the rapid development of sensor and computer technology, the algorithm and technology of vehicle detection have been updated rapidly. But, there are few reviews on vehicle detection of intelligent vehicles, especially covering all kinds of sensors and algorithms in recent years. This article presents a comprehensive review of vehicle detection approaches and their applications in intelligent vehicle systems to analyze the development of vehicle detection, with a specific focus on sensor types and algorithm classification. First, more than 300 research contributions are summarized in this review, including all kinds of vehicle detection sensors (machine vision, millimeter-wave radar, lidar, and multisensor fusion), and the performance of the classic and latest algorithms was compared in detail. Then, the application scenarios of vehicle detection with different sensors and algorithms were analyzed according to their performance and applicability. Moreover, we also systematically summarized the methods of vehicle detection in adverse weather. Finally, the remaining challenges and future research trends were analyzed according to the development of intelligent vehicle sensors and algorithms.

Many recent works detect 3D objects by several sensor modalities for autonomous driving, where high-resolution cameras and high-line LiDARs are mostly used but relatively expensive. To achieve a balance between overall cost and detection accuracy, many multi-modal fusion techniques have been suggested. In recent years, the fusion of LiDAR and Radar has gained ever-increasing attention, especially 4D Radar, which can adapt to bad weather conditions due to its penetrability. Although features have been fused from multiple sensing modalities, most methods cannot learn interactions from different modalities, which does not make for their best use. Inspired by the self-attention mechanism, we present InterFusion, an interaction-based fusion framework, to fuse 16-line LiDAR with 4D Radar. It aggregates features from two modalities and identifies cross-modal relations between Radar and LiDAR features. In experimental evaluations on the Astyx HiRes 2019 dataset, our method outperformed the baseline by 4.20% mAP in 3D and 10.76% BEV mAP for the car class at the moderate level.

This article presents a model-based motion planning and control system for autonomous vehicles and its experimental validation. The system consists of four modules: 1) global routing; 2) behavior planner; 3) local trajectory generation; and 4) trajectory tracking. The algorithm and software of each module are detailed, including a behavior planner with unified models to handle typical scenarios in both highway and urban driving, a deterministic sampling algorithm for robust responsive trajectory generation, and a dynamics-and-delay-aware preview algorithm to achieve accurate trajectory tracking. The developed system is implemented and tested at the Mcity test facility with a full-size automated car and a dozen of challenging traffic scenarios.

This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera. Most previous monocular 3D vehicle detection algorithms focused on cameras on vehicles from the perspective of a driver, and assumed known intrinsic and extrinsic calibration. On the contrary, this paper focuses on the same task using uncalibrated monocular traffic cameras. We observe that the homography between the road plane and the image plane is essential to 3D vehicle detection and the data synthesis for this task, and the homography can be estimated without the camera intrinsics and extrinsics. We conduct 3D vehicle detection by estimating the rotated bounding boxes (r-boxes) in the bird’s eye view (BEV) images generated from inverse perspective mapping. We propose a new regression target called tailed r-box and a dual-view network architecture which boosts the detection accuracy on warped BEV images. Experiments show that the proposed method can generalize to new camera and environment setups despite not seeing imaged from them during training.

Lane detection on road segments with complex topologies such as lane merge/split and highway ramps is not yet a solved problem. This paper presents a novel graph-embedded solution. It consists of two key parts, a learning-based low-level lane feature extraction algorithm, and a graph-embedded lane inference algorithm. The former reduces the over-reliance on customized annotated/labeled lane data. We leveraged several open-source semantic segmentation datasets (e.g., Cityscape, Vistas, and Apollo) and designed a dedicated network that can be trained across these heterogeneous datasets to extract lane attributes. The latter algorithm constructs a graph to represent the lane geometry and topology. It does not rely on strong geometric assumptions such as lane lines are a set of parallel polynomials. Instead, it constructs a graph based on detected lane nodes. The lane parameters in the world coordinate are inferred by efficient graph-based searching and calculation. The performance of the proposed method is verified on both open source and our own collected data. On-vehicle experiments were also conducted and the comparison with Mobileye EyeQ2 shows favorable results.

This paper reports a new continuous 3D loss function for learning depth from monocular images. The dense depth prediction from a monocular image is supervised using sparse LIDAR points, which enables us to leverage available open source datasets with camera-LIDAR sensor suites during training. Currently, accurate and affordable range sensor is not readily available. Stereo cameras and LIDARs measure depth either inaccurately or sparsely/costly. In contrast to the current point-to-point loss evaluation approach, the proposed 3D loss treats point clouds as continuous objects; therefore, it compensates for the lack of dense ground truth depth due to LIDAR's sparsity measurements. We applied the proposed loss in three state-of-the-art monocular depth prediction approaches DORN, BTS, and Monodepth2. Experimental evaluation shows that the proposed loss improves the depth prediction accuracy and produces point-clouds with more consistent 3D geometric structures compared with all tested baselines, implying the benefit of the proposed loss on general depth prediction networks. A video demo of this work is available at https://youtu.be/5HL8BjSAY4Y.

Benefiting from rich resources and virtualization technologies, hybrid cloud has emerged as a promising solution to processing large-scale scientific workflow applications for users in a pay-as-you-go manner. However, considering the complexity of resource configuration and deployment in hybrid clouds, existing workflow scheduling strategies designed for traditional distributed computing systems are limited and powerless. Therefore, for profit-driven infrastructure-as-a-service (IaaS) cloud providers, minimizing makespan and monetary cost of scheduling scientific workflows is an imperative concern. In this paper, we propose two efficient workflow scheduling approaches for hybrid clouds that both consider makespan and monetary cost. Specifically, we first propose a single-objective workflow scheduling optimization approach called DCOH (deadline-constrained cost optimization for hybrid clouds) for minimizing the monetary cost of scheduling workflows under deadline constraint. Based on DCOH, we further propose a multi-objective workflow scheduling optimization approach called MOH (multi-objective optimization for hybrid clouds) for optimizing makespan and monetary cost of scheduling workflows simultaneously. Extensive simulation experiments have been conducted to validate the effectiveness of DCOH and MOH. Simulation results show that our DCOH approach can reduce up to 100.0% monetary cost for users as compared to the competing algorithms under the same deadline constraint and our MOH approach can achieve better cost-makespan trade-off solutions as compared to the competing algorithms.