Connected automated vehicles (CAVs) share information through vehicular networks; however, cyber-attacks on GPS may cause significant challenges to compromise vehicle security and driving safety. In this paper, a novel approach for GPS attack detection and mitigation is proposed using vehicle-to-vehicle (V2V) communication, which enables vehicles to access and utilize accurate location information for autonomous driving. Instead of directly fusing the location data received from other vehicles, a trust evaluation process with a $\chi$2-detector is developed to identify and isolate potential malicious surrounding vehicles that may send erroneous information into the V2V network. Subsequently, a Bayesian approach is employed to fuse data from GPS, inter-vehicle distance, and bearing angle measurements. A real-time Robust-Random-Cut-Forest based detector is constructed to identify possible GPS attacks for an ego vehicle. When a malicious attack is detected, a novel cooperative positioning method is used to mitigate the impact of the GPS attack based on V2V information. Simulation results demonstrate the performance of the proposed approach in detecting GPS attacks timely and improving the positioning accuracy and robustness of CAVs under different attacks.

System dynamics identification significantly impacts trajectory tracking performance for autonomous driving in a dynamic environment. In this paper, a multi-scenario learning model predictive control (MPC) optimization strategy is proposed to reduce model complexity and improve system generalization and robustness. First, the Gaussian process is simplified to reduce the complexity of the system’s residual model while ensuring the optimization problem’s convexity. Then, a meta-learning based multi-scenario model is proposed through online adjusting weight factors to identify the dynamic characteristics when the vehicle drives in a new scenario. Finally, the developed learning model is integrated into a stochastic MPC framework for robust optimization by considering environmental changes and parameter uncertainties. Simulation results show the efficient performance of our proposed method in terms of model prediction accuracy and trajectory tracking.

Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objective function can be solved by employing generalized power iteration and augmented Lagrangian multiplier methods. Experimental results show that the proposed method can more effectively reduce feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is also proved. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.

A common drawback of the EEG applications is that the volume conduction of human head leads to lots of redundant information in EEG recordings. To reduce the redundancy and choose informative EEG features, in this paper, we propose an EEG feature selection technique, termed as Feature Selection with Orthogonal Regression (FSOR). Compared with classical feature selection methods, for nonlinear and nonstationary EEG signals, FSOR can employ orthogonal regression to preserve more discriminative information in the subspace. To verify the EEG feature selection performance, we collected a multichannel EEG dataset for emotion recognition and compared FSOR with two popular feature selection methods. The experimental results demonstrate the advantage of FSOR method over others for reducing the redundant information among the EEG relevant features. Additionally, we found that the absolute power ratio of beta wave to theta wave is the most discriminative feature, and beta band is the critical band for emotion recognition.

In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and min-cost flow, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm. Our source code is available at https://github.com/shenjianbing/submodulartrack.

Crop phenology is of great importance to crop growth monitoring, crop classification and yield prediction. Obtaining accurate phenological information is still difficult due to the complexity of cropping systems. A variety of methods have been utilized in retrieving crop phenology from remotely sensed time-series vegetation index data, among which the so-called threshold method is the mostly used. However, currently the thresholds are set empirically and a validation of the obtained phenological information is often lacking. This paper attempted to investigate the optimal thresholds for retrieving the Start of Season (SOS) and the End of Season (EOS) of different crops from MODIS EVI time-series data by using the dynamic threshold method. The crop growth and development dataset from National Meteorological Information Center of China were used to select analysis samples. The recorded green-up dates or three-leaf dates were used as reference SOS, and the recorded maturity dates were used as reference EOS. Four indicators, Bias, Biassign, Root Mean Square Error (RMSE) and Correlation Coefficient(R) were used to assess the accuracy of the retrieved phenology. Results show that: (1) the often used 20% threshold is not optimum in retrieving crop SOS and EOS. (2) Optimum thresholds are 24% and 48% for retrieving SOS and EOS, respectively. (3) Accuracy assessment results indicate that it is better to set different thresholds in retrieving crop SOS and EOS.

Timely and accurately monitoring of cropland use is of critical significance to ensure food security. Shaanxi province of China has experienced major land use and land cover changes due to the rapid urban sprawl and the implement of the Grain for Green Project. This paper aimed to investigate cropland use changes in Shaanxi from 2000 to 2015 based on MODIS EVI time-series data by utilizing shape-matching cropping index mapping method. The shape-matching method we formerly developed was proved to be an efficient method in mapping cropland use intensity. We firstly divided the study area into six different geomorphic zones, and then randomly selected training samples in each zone. After that, the cropping status for each sample was visually interpreted and used as reference data to train the optimal thresholds for detect cropping index in each zone with the shape-matching cropping index mapping model. Then the cropland use maps for 2000 and 2015 was derived. Finally, the cropland use changes were obtained by spatial analysis of these two cropland use maps. Results show that: (1) The dominant land use is double cropping and non-grain crop production in Guangzhong Plain both in 2000 and 2015. And most cropland in other zones was used for non-grain crop production in the two study years. (2) There were totally 2 585.3 km2cropland newly developed and 7 042.8 km2cropland lost in the past fifteen years. Areas with increased use intensity is 4 084.9 km2, while areas with decreased use intensity is 10 889.3 km2during this period. (3) Extensive cropland use changes were taken places in the Wind Drift Sand Region, the Loess Plateau, the Guanzhong Plain and the Hanjiang Basin. Our findings could provide great support for local agriculture management.

We propose a new metric to predict perceived crosstalk using the original images rather than both the original and ghosted images. The proposed metrics are based on color information. First, we extract a disparity map, a color difference map, and a color contrast map from original image pairs. Then, we use those maps to construct two new metrics (Vdispc and Vdlogc). Metric Vdispc considers the effect of the disparity map and the color difference map, while Vdlogc addresses the influence of the color contrast map. The prediction performance is evaluated using various types of stereoscopic crosstalk images. By incorporating Vdispc and Vdlogc, the new metric Vpdlc is proposed to achieve a higher correlation with the perceived subject crosstalk scores. Experimental results show that the new metrics achieve better performance than previous methods, which indicate that color information is one key factor for crosstalk visible prediction. Furthermore, we construct a new data set to evaluate our new metrics.

The online learning methods are popular for visual tracking because of their robust performance for most video sequences. However, the drifting problem caused by noisy updates is still a challenge for most highly adaptive online classifiers. In visual tracking, target object appearance variation, such as deformation and long-term occlusion, easily causes noisy updates. To overcome this problem, a new real-time occlusion-aware visual tracking algorithm is introduced. First, we learn a novel two-stage classifier with circulant structure with kernel, named integrated circulant structure kernels (ICSK). The first stage is applied for transition estimation and the second is used for scale estimation. The circulant structure makes our algorithm realize fast learning and detection. Then, the ICSK is used to detect the target without occlusion and build a classifier pool to save these classifiers with noisy updates. When the target is in heavy occlusion or after long-term occlusion, we redetect it using an optimal classifier selected from the classifier-pool according to an entropy minimization criterion. Extensive experimental results on the full benchmark demonstrate our real-time algorithm achieves better performance than state-of-the-art methods.

Crop phenology is of critical importance to crop type classification, crop growth monitoring and yield prediction. Data smoothing is an inevitable procedure before extracting crop phenology from remote sensing data. This paper chose Guanzhong Plain in Shaanxi Province, China as the study area to investigate the impacts of the smoothing methods on the extraction of crop phenology. Results show that the double logistic function-fitting is better in describing the overall trend of crop dynamics while the Savitzky-Golay filter can retain more details in vegetation index time series. According to ground observation data, crop phenology retrieved from remote sensing data is not very consistent with the ground observations. However, as for the first growing season, crop phenology derived from the double logistic function-fitting smoothed data tended to closer to observations, and as for the second growing season, that from the Savitzky-Golay filter smoothed data provided better results.

Time-series data of normalized difference vegetation index (NDVI), derived from satellite sensors, can be used to support land-cover change detection and phenological interpretations, but further analysis and applications are hindered by residual noise in the data. As an alternative to a number of existing algorithms developed to compensate for such noise, we develop a simple but computationally efficient method (which we call the changing-weight filter method) to reconstruct a high-quality NDVI time series. The new algorithm consists of two major procedures: (1) detecting the local maximum/minimum points in a growth cycle along an NDVI temporal profile based on a mathematical morphology algorithm and a rule-based decision process and (2) filtering an NDVI time series with a three-point changing-weight filter. This method is tested at 470 test points for 55 vegetation types and a test region in China using a 250-m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product. Comparing our results to those of three other well-known methods-asymmetric Gaussian function fitting, double logistic function fitting, and Savitzky-Golay filtering-the new method has many of the advantages of existing methods, while in some cases, the changing-weight filter method more effectively preserves the curve shape as well as the timing and the amplitude of the local maxima/minima in the NDVI time series for a broad range of phenologies. Moreover, the response of the filtering algorithm is relatively insensitive to the exact values of its design parameters, making the new method more flexible and effective in adjusting to fit a variety of classes of NDVI time series.