This paper comprehensively reviews recent developments in modeling lane-changing behavior. The major lane changing models in the literature are categorized into two groups: models that aim to capture the lane changing decision-making process, and models that aim to quantify the impact of lane changing behavior on surrounding vehicles. The methodologies and important features (including their limitations) of representative models in each category are outlined and discussed. Future research needs are determined.

Recent developments and research needs in modeling lane changing

Driving behavior has a large impact on vehicle fuel consumption. Dedicated study on the relationship between the driving behavior and fuel consumption can contribute to decreasing the energy cost of transportation and the development of the behavior assessment technology for the ADAS system. Therefore, it is vital to evaluate this relationship in order to develop more ecological driving assistance systems and improve the vehicle fuel economy. However, modeling driving behavior under the dynamic driving conditions is complex, making a quantitative analysis of the relationship between the driving behavior and the fuel consumption difficult. In this paper, we introduce two kinds of machine learning methods for evaluating the fuel efficiency of driving behavior using the naturalistic driving data. In the first stage, we use an unsupervised spectral clustering algorithm to study the macroscopic relationship between driving behavior and fuel consumption, using the data collected during the natural driving process. In the second stage, the dynamic information from the driving environment and natural driving data is integrated to generate a model of the relationship between various driving behaviors and the corresponding fuel consumption features. The dynamic environment factors are coded into a processable, digital form using a deep learning-based object detection method so that the environmental data can be linked with the vehicle's operating signal data to provide the training data for the deep learning network. The training data are labeled according to its fuel consumption feature distribution, which is obtained from the road segment data and historical driving data. This deep learning-based model can then be used as a predictor of the fuel consumption associated with different driving behaviors. Our results show that the proposed method can effectively identify the relationship between the driving behavior and the fuel consumption on both macro and micro levels, allowing for end-to-end fuel consumption feature prediction, which can then be applied in the advanced driving assistance systems.

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Impact of Driver Behavior on Fuel Consumption: Classification, Evaluation and Prediction Using Machine Learning

In recent days, the deployment of electric vehicles (EVs) in automobile sector is increasing the load demand in the distribution system. To deal with this load demand, the charging management needs to be improved. Nevertheless, an EV needs several hours to complete charge. Reducing the charging time, energy consumption is a huge contest to deal with the promotion of EV over conventional vehicles. The condition of the itineraries may affect the energy consumption of the EV, which needs to be considered before fulfilling the energy demand. In this article, these are considered assigning suitable charging stations (CS) to individual EVs and their scheduling is taken as an optimization problem. The first part deals with the proper assignment of CS, which is a linear optimization problem and the second deals with the charging scheduling problem. An “intelligent charging scheduling algorithm (ICSA)” is proposed with the integration of Henry gas solubility optimization to minimize total daily price incurred by the CS operator. Later, ICSA is clubbed with other standard optimization techniques considering practical constraints. A 2 m point estimation method has been utilized to tackle the uncertainty and its performance has been compared with the Monte-Carlo simulation technique. The robustness of ICSA is evaluated and confirmed using the Wilcoxon signed rank test and Quade test.

Charging Scheduling of Electric Vehicle Incorporating Grid-to-Vehicle and Vehicle-to-Grid Technology Considering in Smart Grid

The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.

Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning

Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how driving behaviors and energy consumption correlate with each other and to what extent these factors related to connected vehicles can influence the motion prediction performance. The precise recognition of driving behaviors and prediction of the vehicle motion is critical to the driving safety for connected automated vehicles (CAVs). Hence, in this study, an energy-aware driving pattern analysis and motion prediction system are proposed for CAVs using a deep learning-based time-series modeling approach. First, energy-aware longitudinal acceleration and deceleration behaviors and lateral lane-change behaviors are statistically analyzed. Then, a sliding standard deviation (SSD) test is applied to evaluate the smoothness of the trajectory and velocity signals considering different energy consumption levels. An energy-aware personalized joint time-series modeling (PJTSM) approach based on a deep recurrent neural network (RNN) and long short-term memory (LSTM) cell are proposed for accurate motion (trajectory and velocity) prediction of the leading vehicle. Finally, the differences in the prediction performance regarding different energy consumption levels are compared and discussed. It is shown that due to the higher randomness of the driving behaviors, the prediction accuracy for heavy energy users is the lowest among the three categories, which means it is harder to anticipate the driving behaviors of cars exhibiting heavy energy consumption. The personalized estimation of driving behaviors of CAVs will contribute to safer automated driving and transportation systems.

/pdf/toward-safe-and-smart-mobility-energy-aware-deep-learning-dnvb9nmanv.pdf

Toward Safe and Smart Mobility: Energy-Aware Deep Learning for Driving Behavior Analysis and Prediction of Connected Vehicles

Measurement or evaluation of driving behavior is vital to the development of advanced driver assistance systems (ADAS). Accurate assessment of a driver's behavior allows the ADAS to make appropriate recommendations and function in harmony with the driver by adapting to the driver's personal driving style. Traditional driving behavior analysis methods focus on driving process modeling, which is complex and rarely be generalized for some special application like fuel consumption evaluation. In particular, the complexity of the modeling process, in combination with dynamic driving conditions, makes the quantitative analysis of the relationship between driving behavior and fuel consumption difficult to evaluate. Therefore, in this paper we propose a data mining-based method which can be used to measure the driving behavior's effect on fuel consumption by using real-world driving data. First, we introduce a spectral clustering algorithm which will be used with driving data to group drivers according to their driving styles. Then, we design a driving behavior evaluation experiment with fixed traffic conditions and a wireless data collection platform to collect the data. A data processing mechanism is also proposed to obtain the proper experimental driving data suitable to be the spectral clustering algorithm. Finally, we use actual fuel consumption as the ground truth to verify the accuracy of our method. The results show that the proposed method can measure the driving behavior's effect on the vehicle's fuel consumption well.

Yang Xu

Papers

Impact of Driver Behavior on Fuel Consumption: Classification, Evaluation and Prediction Using Machine Learning

Spectral clustering based approach for evaluating the effect of driving behavior on fuel economy