In this paper, we propose a novel localization methodology to enhance the accuracy from two aspects, i.e., adapting to the uncertain noise of microelectromechanical system-based inertial navigation system (MEMS-INS) and accurately predicting INS errors. First, an interacting multiple model (IMM)-based sequential two-stage Kalman filter is proposed to fuse the information of MEMS-INS, global positioning system (GPS), and in-vehicle sensors. Three bias filters are built with different covariance matrices to cover a wide range of noise characteristics. Then, IMM algorithm provides a soft switching among the three bias filters to adapt to the uncertain noise of MEMS-INS. Further, an elaborate predictor is developed to accurately predict INS errors during GPS outages. The elaborate predictor comprises an online trained autoregressive integrated moving average (ARIMA) model and an offline trained extreme learning machine (ELM) model. The ARIMA model is designed to predict the basic accumulation process of INS errors, while the ELM model is designed to correct the errors caused by the changes of noise characteristics. Thus, the INS errors can be properly compensated when GPS observations are not available. In all, the proposed localization methodology can achieve accurate performance when facing uncertain noises of MEMS-INS and GPS outages simultaneously. To verify the effectiveness of the proposed methodology, road test experiments with various driving scenarios were performed. The experimental results illustrate the feasibility and effectiveness of the proposed methodology.

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.

In large-scale long-term dynamic environments, high-frequency dynamic objects inevitably lead to significant changes in the appearance of the scene at the same location at different times, which is catastrophic for place recognition (PR). Therefore, how to eliminate the influence of dynamic objects to achieve robust PR has universal practical value for mobile robots and autonomous vehicles. To this end, we suggest a novel semantically consistent LiDAR PR method based on chained cascade network, called SC_LPR, which mainly consists of a LiDAR semantic image inpainting network (LSI-Net) and a semantic pyramid Transformer-based PR network (SPT-Net). Specifically, LSI-Net is a coarse-to-fine generative adversarial network (GAN) with a gated convolutional autoencoder as the backbone. To effectively address the challenges posed by variable-scale dynamic object masks, we integrate the updated Transformer block with mask attention and gated trident block into LSI-Net. Sequentially, in order to generate a discriminative global descriptor representing the point cloud, we design an encoder with pyramid Transformer block to efficiently encode long-range dependencies and global contexts between different categories in the inpainted semantic image, followed by an augmented NetVALD, a generalized VLAD (Vector of Locally Aggregated Descriptors) layer that adaptively aggregates salient local features. Last but not least, we first attempt to create a LiDAR semantic inpainting dataset, called LSI-Dataset, to effectively validate the proposed method. Experimental comparisons show that our method not only improves semantic inpainting performance by about 6%, but also improves PR performance in dynamic environments by about 8% compared to the representative optimal baseline. LSI-Dataset will be publicly available at https://github.KD.LPR.com/.

In this paper, we propose a cost-effective localization solution for land vehicles, which can simultaneously adapt to the uncertain noise of inertial sensors and bridge Global Positioning System (GPS) outages. First, three Unscented Kalman filters (UKFs) with different noise covariances are introduced into the framework of Interacting Multiple Model (IMM) algorithm to form the proposed IMM-based UKF, termed as IMM-UKF. The IMM algorithm can provide a soft switching among the three UKFs and therefore adapt to different noise characteristics. Further, two IMM-UKFs are executed in parallel when GPS is available. One fuses the information of low-cost GPS, in-vehicle sensors, and micro electromechanical system (MEMS)-based reduced inertial sensor systems (RISS), while the other fuses only in-vehicle sensors and MEMS-RISS. The differences between the state vectors of the two IMM-UKFs are considered as training data of a Grey Neural Network (GNN) module, which is known for its high prediction accuracy with a limited amount of samples. The GNN module can predict and compensate position errors when GPS signals are blocked. To verify the feasibility and effectiveness of the proposed solution, road-test experiments with various driving scenarios were performed. The experimental results indicate that the proposed solution outperforms all the compared methods.

In this paper,an adaptive steering robust nonlinear control methodbased on driver behavior is presented for an unmanned driving robotic vehicle (UDRV),in order to achieve accurate and stable steering control of UDRV.Considering modeling uncertainties and unknown nonlinear external disturbances, a UDRV nonlinear dynamics model is established. A driver behavior model is established including a coordinate transformation model, a driver virtual path planning model, and a driver desired yaw rate model. Basis of this, anadaptive steering robust nonlinear controller for UDRV is presented, consisted of an adaptive robust backstepping controller and a nonlinear disturbance observer (NDO). TheNDO is designed to compensate for modeling uncertainties and unknown nonlinear external disturbances. The adaptive steering robust nonlinear controller for UDRV is designed through backstepping method and NDOcompensation. The stability of controlsystem is proved. Finally, double lanechange experiments are conducted.Comparison analysis results among the proposed control method, other existed control method and human driver demonstrate the effectiveness of the proposed method.

Many intelligent transportation system applications require accurate, reliable, and continuous vehicle positioning. How to achieve such positioning performance in extended GPS-denied environments such as tunnels is the main challenge for land vehicles. This paper proposes a hybrid multi-sensor fusion strategy for vehicle positioning in tunnels. First, the preliminary positioning algorithm is developed. The Radio Frequency Identification (RFID) technology is introduced to achieve preliminary positioning in the tunnel. The received signal strength (RSS) is used as an indicator to calculate the distances between the RFID tags and reader, and then a Least Mean Square (LMS) federated filter is designed to provide the preliminary position information for subsequent global fusion. Further, to improve the positioning performance in the tunnel, an interactive multiple model (IMM)-based global fusion algorithm is developed to fuse the data from preliminary positioning results and low-cost in-vehicle sensors, such as electronic compasses and wheel speed sensors. In the actual implementation of IMM, the strong tracking extended Kalman filter (STEKF) algorithm is designed to replace the conventional extended Kalman filter (EKF) to achieve model individual filtering. Finally, the proposed strategy is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed strategy.