Timely recognition of driving intention is crucial in the design of a safe and effective driving assistance system. This study proposes an efficient recognition approach based on Nonlinear Polynomial Regression (NPR) and Recurrent Hidden Semi-Markov Model (R-HSMM) to recognize the driver lane-change intention accurately in the early stage. The NPR model is utilized to transform the input signal amplitude into the standard form in order to improve system adaptability. Besides, an unsupervised time-series segmentation method named the Toeplitz Inverse Covariance-based Clustering (TICC) is applied to label the driving data automatically. The R-HSMM is utilized as a time-series classifier to classify the driving intention during the lane-change process into predefined categories based on the signals processed by the NPR. The proposed method is verified by the experiments with a driving simulator. The experimental results show that the proposed method can recognize driver intention earlier than the popular recognition methods, and also can reduce the number of false warnings during the lane-change process, which has great significance for driving safety improvement. Moreover, the proposed method can adapt well to various vehicle speeds achieving stable recognition performance.

In this paper, we present a complete, flexible and safe convex-optimization-based method to solve speed planning problems over a fixed path for autonomous driving in both static and dynamic environments. Our contributions are five fold. First, we summarize the most common constraints raised in various autonomous driving scenarios as the requirements for speed planner developments and metrics to measure the capacity of existing speed planners roughly for autonomous driving. Second, we introduce a more general, flexible and complete speed planning mathematical model including all the summarized constraints compared to the state-of-the-art speed planners, which addresses limitations of existing methods and is able to provide smooth, safety-guaranteed, dynamic-feasible, and time-efficient speed profiles. Third, we emphasize comfort while guaranteeing fundamental motion safety without sacrificing the mobility of cars by treating the comfort box constraint as a semi-hard constraint in optimization via slack variables and penalty functions, which distinguishes our method from existing ones. Fourth, we demonstrate that our problem preserves convexity with the added constraints, thus global optimality of solutions is guaranteed. Fifth, we showcase how our formulation can be used in various autonomous driving scenarios by providing several challenging case studies in both static and dynamic environments. A range of numerical experiments and challenging realistic speed planning case studies have depicted that the proposed method outperforms existing speed planners for autonomous driving in terms of constraint type covered, optimality, safety, mobility and flexibility.

This paper presents a model predictive control (MPC) scheme for the stabilization of high-speed autonomous ground vehicles (AGVs) considering the effect of road topography. Accounting for the road curvature and bank angle, a single-track dynamic model with roll dynamics is derived. Variable time steps are utilized for vehicle model discretization, enabling collision avoidance in the long-term without compromising the prediction accuracy in the near-term. Accordingly, safe driving constraints including the sideslip envelope, zero-moment-point and lateral safety corridor are developed to handle stability and obstacle avoidance. Taking these constraints into account, an MPC problem is formulated and solved at each step to determine the optimal steering control commands. Moreover, feedback corrections are integrated into the MPC to compensate the unmodeled dynamics and parameter uncertainties. Comparative simulations validate the capability and real-time ability of the proposed control scheme.

Nowadays, vision oriented intelligent vehicle systems such as autonomous driving or transportation assistance can be optimized by enhancing the visual visibility of images acquired in bad weather conditions. The presence of haze in such visual scenes is a critical threat. Image dehazing aims to restore spatial details from hazy images. There have emerged a number of image dehazing algorithms, designed to increase the visibility of those hazy images. However, much less work has been focused on evaluating the visual quality of dehazed images. In this paper, we propose a Reduced-Reference dehazed image quality evaluation approach based on Partial Discrepancy (RRPD) and then extend it to a No-Reference quality assessment metric with Blind Perception (NRBP). Specifically, inspired by the hierarchical characteristics of the human perceiving dehazed images, we introduce three groups of features: luminance discrimination, color appearance, and overall naturalness. In the proposed RRPD, the combined distance between a set of sender and receiver features is adopted to quantify the perceptually dehazed image quality. By integrating global and local channels from dehazed images, the RRPD is converted to NRBP which does not rely on any information from the references. Extensive experiment results on both synthetic and real dehazed image quality databases demonstrate that our proposed methods outperform state-of-the-art full-reference, reduced-reference, and no-reference quality assessment models. Furthermore, we show that the proposed dehazed image quality evaluation methods can be effectively applied to tune parameters for image dehazing algorithms and have the potential to be deployed in real transportation systems.

To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.