The rapid development of big data and artificial intelligence (AI) is revolutionizing the automotive and transportation industries, leading to the creation of the Autonomous Modular Bus (AMB). Designed to address the key challenges of modern public transportation systems, the AMB adopts a modular dynamic assembly approach. However, existing research on the AMB predominantly focuses on operational aspects, whereas in-transit docking remains the primary obstacle to its commercial deployment. This challenge stems from the fact that current perception accuracy in autonomous vehicles is limited to the decimeter level, with insufficient capability to manage adverse weather and complex traffic conditions. To enable AMBs to achieve full-scenario autonomous driving capabilities, this paper reviews current perception technologies from three perspectives: single-vehicle single-sensor perception, multi-sensor fusion perception, and cooperative perception. It examines the characteristics of existing perception solutions and evaluates their applicability to AMB-specific requirements. Furthermore, considering the unique challenges of in-transit docking, this paper identifies and proposes four future research directions for advancing AMB perception systems as well as general autonomous driving technologies.

Within autonomous driving research, the intricate variability of the road surface is frequently overlooked, while the tire-road interactions critically impact vehicle stability. This paper comprehensively reviews traditional and emerging tire-road adhesion coefficient (TRAC) estimation methods for intelligent vehicles. We initially categorize traditional methods into cause-based and effect-based approaches, which are founded on vehicle responses and road surface characteristics, respectively. Then, we classify emerging methods into learning-based approaches and hybrid models combining physical principles with data-driven strategies. We eventually point out areas for improvement and future research directions. The proposed systematic taxonomy summarizes the independent and collaborative operations of dynamics analysis and learning methods in TRAC estimation, offering insights for further research.

Curve Speed Warning (CSW) systems assist drivers in adjusting speeds before entering a curve to improve road safety. As an essential part of CSW, the safe speed model is key in determining the speed trajectory. Current safe speed models are mostly based on the theoretical line shape of the road, which leads to the neglect of the driving differences, and it is likely to result in unreasonable speed guidance. This paper proposes a more comprehensive method to provide a safe speed trajectory in advance and enhance safety for trucks with heavy loads when approaching curve-slope sections. First, a classification model applying a random forest algorithm is developed to output the critical safe speed in a specific scenario. Second, a variable speed limit algorithm for a given path is framed, minimizing fuel and travel time consumption, and then embedded with a variable speed limit determination process. Simulation experiments are implemented based on real-world paths to verify the proposed structure. The findings indicate that our model is capable of generating speed trajectories adaptively. Additionally, experiments underscore the significant influence that the weight of the load and its center of gravity (CG) exert on the stability assessment of trucks, as we conclude that the optimal loading strategy for trucks is to reach a full load and avoid the load’s lateral offset.

Traffic flow prediction in spatio-temporal networks is a crucial aspect of Intelligent Transportation Systems (ITS). Existing traffic flow forecasting methods, particularly those utilizing graph neural networks, encounter limitations. When processing large-scale graph data, the depth of these models can restrict their ability to effectively capture complex relationships and patterns. Additionally, these methods often focus mainly on local neighborhood information, which can limit their capability to recognize and analyze global relationships and patterns within the graph data. Therefore, we proposed a deep knowledge distillation model, tailored to effectively capture spatio-temporal patterns in traffic flow prediction. This model incorporates a bidirectional random walk process on a directed graph, enabling it to effectively capture both spatial and temporal dependencies. Utilizing a blend of mutual learning and self-distillation, our approach enhances the detection of spatio-temporal relationships within traffic data and improves the feature perception ability at both local and global levels. We tested our model on two real-world datasets, achieving notable improvements in prediction accuracy, especially for predictions within a one-hour timeframe. In comparison to the baseline model, our proposed model achieved accuracy improvements of 0.19 and 0.18 on the respective datasets. These results highlight the success of using mutual learning and self-distillation to transfer knowledge effectively within and between models and to improve the model’s capability in identifying and extracting features.

Accurate transfer time prediction and future transfer time information are important for both public transport operators and passengers. However, existing studies cannot effectively manage high-dimensional transfer time data, capture the complex nonlinearity of transfer time, or provide accurate transfer time information. This study provides a reliable prediction model called low-rank Bayesian temporal matrix factorization (LBTMF) to address these challenges. First, on the basis of a high-dimensional spatiotemporal matrix of transfer time data, we develop a low-rank temporal-regularized matrix factorization-based imputation module to capture spatial and temporal characteristics to replace missing transfer time data. Second, to further predict the transfer time with the imputation of missing data, we propose the spatiotemporal-based Bayesian temporal matrix factorization prediction module to recover hourly and daily regular characteristics to predict the transfer time at different metro stations during various periods. Finally, the comprehensive experimental findings suggest that the LBTMF model outperforms other excellent approaches in terms of imputation efficiency, prediction accuracy, and robustness.

Accurate visual perception and comprehensive scene understanding are critical for the safety and reliability of autonomous vehicles (AVs). Nevertheless, the efficacy of visual perception systems can be impaired by the intricacy of road scenes, and the existing scene understanding approach may be insufficient. Consequently, this study proposes an enhanced scene understanding model to achieve precise awareness of driving situations. Recognizing the limitations posed by the oversimplification of samples in current urban scene datasets, we selected critical frames from 336000 video frames, sourced from real-world driving environments, to assemble a more complex road scene (CRS) dataset. We integrated Residual Neural Network and pyramid scene parsing network architectures and refined them through class mapping and targeted network fine-tuning. Based on the segmentation outputs and the XGBoost algorithm, we identified the driving scenarios for the ego vehicle, enabling instantaneous driving situation analysis. The predictive model also evaluated the trajectory of interactive vehicles and estimated their kinematic states. Furthermore, we have conducted a thorough evaluation of scenario complexity, integrating the features described above. The findings indicate that our model achieves a segmentation accuracy of 78.8% in CRSs, with a twofold improvement in training efficiency. We also confirmed the effectiveness of the scene understanding approach through real-world road testing in China. This research provides insight into situation awareness within CRSs, thereby enhancing the visual perception capabilities of AVs. The implications of these results are substantial for their application in autonomous driving tests and advancing decision-making and control algorithms.

We investigate how to effectively and efficiently embed users’ personalized travel behaviors to vectors in this paper. Based on an example scenario of travel mode choice in intelligent transportation system, three data structures representing users’ travel behaviors are defined, namely heterogeneous graph of users’ travel behaviors, user travel behavior$k$-partite graph, and personalized user travel behavior sentence set. This paper systematically analyzes the principle of existing methods and provides intuitions for the problem of learning travel behavior representation in intelligent transportation system. Then we propose the Behavior2vector, which is an improved method tailored for embedding users’ personalized travel behaviors to vectors. In our experiments, we design a travel mode choice model based on machine learning, which uses both hand-crafted basic features and embedded vector features. We further quantify the impact of various factors on travel mode choice and use travel big data to test the hypothesis of traffic assignment models, e.g., travelers always choose the path with the shortest path. In addition, we also compared with the existing graph embedding methods and essentially discussed their advantages and disadvantages.

The integrity of bus route information is fundamental to the analysis of operating status and travel pattern of urban public transport system. However, due to the malfunction of bus positioning devices or delayed update of database, the route information stored in positioning devices might be lost or erroneous. To address this issue, this paper designs a framework matching the bus trajectory with a set of predefined bus routes. The trajectories are first partitioned into segments using the spatio-temporal DBSCAN. Then, the curve similarity between trajectories and bus routes is calculated based on the metric of partial Fréchet distance, which searches for a best mapping between curves that minimizes the maximum distance between vertex pairs. A directed-acyclic-graph-based method is also proposed to compute the partial Fréchet distance. Finally, the best match for each trajectory is given based on the relative ranking. The proposed framework is evaluated on the bus trajectory data of Fuyang and Shenzhen in China. The experimental results demonstrate that the framework can well identify the underlying routes according to the recorded bus trajectories and suggest which route information needs updating.

The accurate prediction of online taxi-hailing demand is challenging but of significant value in the development of the intelligent transportation system. This article focuses on large-scale online taxi-hailing demand prediction and proposes a personalized demand prediction model. A model with two attention blocks is proposed to capture both spatial and temporal perspectives. We also explored the impact of network architecture on taxi-hailing demand prediction accuracy. The proposed method is universal in the sense that it is applicable to problems associated with large-scale spatiotemporal prediction. The experimental results on city-wide online taxi-hailing demand dataset demonstrate that the proposed personalized demand prediction model achieves superior prediction accuracy.

Deep Neural Network (DNN) has been applied in a wide range of fields due to its exceptional predictive power. In this paper, we explore how to use DNN to solve the large-scale bus passenger flow prediction problem. Currently, most existing methods designed for the passenger flow prediction problem are based on a single view, which is insufficient to capture the dynamics in passenger flow fluctuation. Thus, we analyze the passenger flow from scopes on both macroscopic and microscopic levels, in order to take full advantage of the information from a variety of views. To better understand the role of different views, decision-tree-based models are used in modeling and predicting passenger flow. The defects and key features of decision-tree-based models are then analyzed. The results of the analysis can assist the architecture design of the deep learning network. Inspired by the feature engineering of decision-tree-based models, a modular convolutional neural network is designed, which contains automatic feature extraction block, feature importance block, fully-connected block, and data fusion block. The proposed model is evaluated on the city-wide public transport datasets in Nanjing, China, involving 1,091 bus lines in total. The experiment results demonstrate the outstanding performance of the proposed method in real situations.

Accurate demand prediction plays a significant role in online car-hailing platforms. With ensemble learning, several models can be combined into a single demand predictive model, achieving low prediction error. Nevertheless, the existing ensemble methods are not intended for spatio-temporal data and thus cannot deal with it. In this article, a spatio-temporal data ensemble model is proposed to predict car-hailing demands. Treating the prediction results as various channels of an image, the proposed ensemble module first compresses and then restores the results using the fully convolutional network. Additionally, a skip connection is used to preserve both the fine-grained information in the shallow layers and the deep coarse information. Based on the principle of model as a service, any model can be plugged into our framework as base models to improve the prediction accuracy. Experimental results demonstrate the effectiveness of the presented model.

How to effectively ensemble different base models is a challenging but extremely valuable task. This study focuses on the construction of an ensemble framework designed for spatio-temporal data to predict large-scale online taxi-hailing demand, where an attention-based deep ensemble net is designed to enhance the prediction accuracy. We present three attention blocks to model the inter-channel relationship, inter-spatial relationship and position relationship of the feature maps. Then, the attention maps can be multiplied by the input feature map for adaptive feature refinement. The proposed method is a kind of commonly used ensemble method which applies to large-scale spatio-temporal prediction. Experimental results on city-wide online taxi-hailing demand predictions demonstrate that our proposed attention-based ensemble net is superior to the existing ensemble strategy in terms of the prediction accuracy.