In this paper, we propose a deep neural network-based car-following model that has two distinctive properties. First, unlike most existing car-following models that take only the instantaneous velocity, velocity difference, and position difference as inputs, this new model takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs. That is, we assume that drivers’ actions are temporally dependent in this model and try to embed prediction capability or memory effect of human drivers in a natural and efficient way. Second, this car-following model is built in a data-driven way, in which we reduce human interference to the minimum degree. Specially, we use recently developing deep neural networks rather than conventional neural networks to establish the model, since deep learning technique provides us more flexibility and accuracy to describe complicated human actions. Tests on empirical trajectory records show that this deep neural network-based car-following model yield significantly higher simulation accuracy than existing car-following models. All these findings provide a novel way to study traffic flow theory and traffic simulations.

Capturing Car-Following Behaviors by Deep Learning

Autonomous lane change maneuver was developed using cooperative strategy.Proposed system can be potential to prevent lane change crashes and thus reducing injuries and fatalities.A trajectory planning method based on polynomial was developed.A trajectory tracking controller with global convergence ability was designed.Simulations and experimental results were presented to validate the method. Lane change maneuver is one of the most conventional behaviors in driving. Unsafe lane change maneuvers are key factor for traffic accidents and traffic congestion. For drivers' safety, comfort and convenience, advanced driver assistance systems (ADAS) are presented. The main problem discussed in this paper is the development of an autonomous lane change system. The system can be extended applied in intelligent vehicles in future. It solves two crucial issues - trajectory planning and trajectory tracking. Polynomial method was adopted for describing the trajectory planning issue. Movement of a host vehicle was abstracted into time functions. Moreover, collision detection was mapped into a parameter space by adopting infinite dynamic circles. The second issue was described by backstepping principle. According to the Lyapunov function, a tracking controller with global convergence property was verified. Both the simulations and the experimental results demonstrate the feasibility and effectiveness of the designed method for autonomous lane change.

Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system

In order to drive safely and efficiently on public roads, autonomous vehicles will have to understand the intentions of surrounding vehicles, and adapt their own behavior accordingly. If experienced human drivers are generally good at inferring other vehicles' motion up to a few seconds in the future, most current Advanced Driving Assistance Systems (ADAS) are unable to perform such medium-term forecasts, and are usually limited to high-likelihood situations such as emergency braking. In this article, we present a first step towards consistent trajectory prediction by introducing a long short-term memory (LSTM) neural network, which is capable of accurately predicting future longitudinal and lateral trajectories for vehicles on highway. Unlike previous work focusing on a low number of trajectories collected from a few drivers, our network was trained and validated on the NGSIM US-101 dataset, which contains a total of 800 hours of recorded trajectories in various traffic densities, representing more than 6000 individual drivers.

An LSTM Network for Highway Trajectory Prediction

Lane-changing (LC), which is one of the basic driving behavior, largely impacts on traffic efficiency and safety. Modeling an LC process is challenging due to the complexity and uncertainty of driving behavior. To address this issue, this paper proposes a data-driven LC model based on deep learning models. Deep belief network (DBN) and long short-term memory (LSTM) neural network are employed to model the LC process that is composed of LC decisions (LCD) and LC implementation (LCI). The empirical LC data provided by Next Generation Simulation project (NGSIM) is utilized to train and test the proposed DBN-based LCD model and LSTM-based LCI model. The results indicate that the proposed data-driven model is able to accurately predict the LC process of a vehicle. The sensitivity analysis shows that the most important factor associated with LCD is the relative position of the preceding vehicle in the target lane. This may be the first work that comprehensively models LC using deep learning approaches.

A data-driven lane-changing model based on deep learning

Asymmetric driving behavior is a critical characteristic of human driving behaviors and has a significant impact on traffic flow. In consideration of the asymmetric driving behavior, this paper proposes a long short-term memory (LSTM) neural networks (NN) based car-following (CF) model to capture realistic traffic flow characteristics by incorporating the driving memory. The NGSIM data are used to calibrate and validate the proposed CF model. Meanwhile, three characteristics closely related to the asymmetric driving behavior are investigated: hysteresis, discrete driving, and intensity difference. The simulation results show the good performance of the proposed CF model on reproducing realistic traffic flow features. Moreover, to further demonstrate the superiority of the proposed CF model, two other CF models including recurrent neural network based CF model and asymmetric full velocity difference model, are compared with LSTM-NN model. The results reveal that LSTM-NN model can capture the asymmetric driving behavior well and outperforms other models.

A car-following model considering asymmetric driving behavior based on long short-term memory neural networks

Reaction delay of the driver–vehicle unit varies greatly according to driver–vehicle characteristics and traffic conditions, and is an indispensable factor for modeling vehicle movements. In this study, by defining the time interval between the relative speed and acceleration, the gap and speed observed from real traffic as driver–vehicle reaction delay, a neural network for instantaneous reaction delay is built. Incorporating the reaction delay network into a neural-network-based car-following model, movements of nine vehicles which follow each other are simulated. Simulation results show that the models with instantaneous reaction delay apparently outperform the models with fixed reaction delay. In addition, the model with short fixed reaction delay makes the vehicles follow each other more closely than the vehicles in real traffic do, and collisions occur in the model with long fixed reaction delay, which also illustrates the necessity of taking into account instantaneous reaction delay in microscopic traffic simulation. Besides, for future reference, the calibrated weights and biases in the proposed methodology are presented in Appendix.

Car-following behavior with instantaneous driver–vehicle reaction delay: A neural-network-based methodology

Predicting driver’s lane-changing decisions using a neural network model

Evaluation of Car-following Models Using Trajectory Data from Real Traffic

A Car-following Model Based on Discrete Choice Theory

1Student Member of JSCE, Graduate School of Engineering, Nagoya Institute of Technology (Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan) E-mail: zhengjian1112@gmail.com 2Member of JSCE, Associate Professor, Graduate School of Engineering, Nagoya Institute of Technology (Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan) E-mail: suzuki.koji@nitech.ac.jp 3Member of JSCE, Professor, Graduate School of Engineering, Nagoya Institute of Technology (Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan) E-mail: fujita.motohiro@nitech.ac.jp

Jian Zheng

Papers

Car-following behavior with instantaneous driver–vehicle reaction delay: A neural-network-based methodology

Predicting driver’s lane-changing decisions using a neural network model

Evaluation of Car-following Models Using Trajectory Data from Real Traffic

A Car-following Model Based on Discrete Choice Theory

Modeling acceleration and deceleration behavior in hov lane by using discrete choice theory