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At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks. [ABSTRACT FROM AUTHOR]

Urban road speed prediction is helpful to guide drivers to choose unimpeded routes, reduce waiting time and improve travel efficiency. Urban traffic conditions are affected by many factors. Based on the consideration of various traffic flow characteristic data and weather data, a combined model of road travel speed prediction based on long short-term memory (LSTM) cyclic neural network is established. The Didi floating car data in the area around the South Second Ring Road of Xi'an city are selected to predict the road travel speed by extracting the traffic flow characteristics (speed, flow, acceleration and stopping times) and weather characteristics (temperature, humidity, weather and wind speed) of the data set. The results show that compared with LSTM model, BP neural network model and SVR model without external features, the mean absolute error, mean square error and determination coefficient of the combined model with multi-feature data are 2. 695, 13. 838 and 0. 771, and the confidence interval of (-1. 235, 1. 795) is better than other models. The combined model has higher accuracy and stability. [ABSTRACT FROM AUTHOR]

Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction. [ABSTRACT FROM AUTHOR]

At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks.

Customized path-based speed prediction is an eventful tool for congestion avoidance, route optimization and travel time prediction for navigation apps, cab-hailing companies and autonomous vehicles. Traditionally, the speed prediction algorithms are based on road segments and can only support several main roads. Path-based speed prediction is very challenging since the speed is always changing in different path locations and is jointly affected by lots of complicated factors. This article presents a novel deep learning framework for customized path-based speed prediction. A Path-based Speed Prediction Neural Network (PSPNN) is designed to achieve speed predictions for a given path and attributes information. A hierarchical Convolutional Neural Network (CNN) and deep Bidirectional Long Short-Term Memory (Bi-LSTM) structure for different kinds of feature extraction are applied for multiple levels: the path cell, sub-path and the whole path. The method narrows down the prediction unit from road segments to customized path cells (mean length: 59.52m) and achieves a mean absolute error (MAE) of 1.94 m/s and Mean Absolute Percentage Error (MAPE) of 18.14%, showing the potential of serving rigorous data-driven applications. So far, PSPNN is the first made-to-order path-based speed prediction algorithm and can help both travelers and managers to obtain large-scale bespoke paths speed information in advance. [ABSTRACT FROM AUTHOR]

Real-time traffic speed prediction is an essential component of intelligent transportation systems applications on large-scale urban networks, e.g., proactive traffic management, advanced information provision, and prompt incident response. The family of traffic prediction models (e.g., convolutional neural networks) based on multi-detector speed diagrams in the time-space plane has been one of the most frequently used approaches for individual roads and the entire network. However, the predefined stacking sequence of traffic detectors along the spatial dimension of the speed diagram has a significant influence on the prediction performance, which makes network-wide speed prediction more challenging. To tackle the above challenge and better capture complicated traffic dynamics, we propose a novel speed prediction approach, named spatial–temporal deep tensor neural networks (ST-DTNN), for a large-scale urban network with mixed road types. Spatial and temporal dependencies of different road segments are simultaneously taken into account to improve the network-wide prediction accuracy. A scalable deep tensor is constructed for the ST-DTNN to eliminate the potentially negative impact caused by the manually stacking sequence of speed time series collected at different locations. Multi-step ahead traffic speeds can be simultaneously predicted based on probe data for a real-world large-scale urban network with hundreds of detectors installed on freeways, highways, and major/minor arterials. The results demonstrate the capability and effectiveness of the proposed ST-DTNN approach. Compared with the benchmark models, the ST-DTNN performs higher prediction accuracy during either peak or off-peak periods within an acceptable training time and has more stable prediction performance on the spatial scale. The proposed approach can be extended to develop network-wide traffic state monitoring, optimize routing in navigation services, and support congestion mitigation. [ABSTRACT FROM AUTHOR]

This paper aims to incorporate the throttle handle level prediction in high speed train(HST) operation prediction problem to enable the prediction of HST drivers' activities, in which the key instructions available to HST driver are difficult to determine. Specifically, we consider an end-edge-cloud orchestration system to capture the real-time responses for driver state changes. By adding edge computing nodes, the real-time performance of data collection, transmission, and processing is improved. Our ultimate goal is to guide and regulate train drivers' activities in the same way, regardless of uncertain factors affecting HST dynamic or kinematic performance. We formulate the problem as a physical-based and data-driven deep learning-aided prediction model and solve it using a novel long short-term memory (LSTM) deep neural network which combines: (i) an off-line approximate training model to learn the time series data in the cloud layer, and (ii) an online prediction process to determine driving strategies in the real-time windows, more in general expressed as driving skill level constraints. To evaluate the performance of our approach, some case studies using the real-world railway infrastructure and HST data have been conducted. The results show that the proposed models produce higher prediction accuracy for both speed and throttle handle level prediction tasks. Compared to the conventional HST operation prediction problem, which considers speed sequences only without throttle handle level consideration, this study finds that jointly modeling speed and throttle handle level actually improves the next operation prediction performance itself, potentially because throttle handle level observations capture the information on HST control dynamics, which may affect operators' driving choices. • Structure. Edge computing and prediction scheme are innovatively combined with HST data collection to meet the delay-sensitive and sequential-aware operation requirements, and a novel HST operation structure for end-edge cloud orchestration system is built. • Modeling. A combination of the physical-based and the data-driven deep learning model is formulated for HST operation prediction under uncertainty in throttle handle level and speed, which is a new model in the literature. As discussed, the existing research has only considered speed dynamics in real-time DAS limitedly. • Practice. From the field collected data in Beijingxi–Zhengzhoudong HSR (as shown in Figure 5), we test the effectiveness of our proposed model with seven cases. Since the real-time driving advice is calculated in a few milliseconds, our two-phase solution method can be practically relevant when designing DAS or ATO systems. More importantly, most time-consuming calculations are executed off-line in the cloud layer and the amount of on-line calculations in edge layer is limited to looking up a table. [ABSTRACT FROM AUTHOR]

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy.

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.

Abstract In this paper, a data driven approach, deep learning, for vehicle speed prediction is presented. Deep learning based on the deep neural network structure is applied to predict a future short-term speed with the collected dataset including the historical vehicle speed and its corresponding acceleration, steering information, location and driving date. The influence of the driving factors on the accuracy of vehicle speed prediction is analyzed. And four standard driving cycles are used to test the generalization ability of the proposed speed prediction method. The results show that when the training set is the information of the historical speed and the driving date, the prediction effect is the best, and RMSE is 1.5298. And the proposed prediction method has good generalization ability. [ABSTRACT FROM AUTHOR]

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy. [ABSTRACT FROM AUTHOR]

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs. [ABSTRACT FROM AUTHOR]