Lane Change Intention Recognition for Intelligent Connected Vehicle Using Trajectory Prediction

Recent research on the prediction of driver’s lane-changing behaviour requires vehicle surrounding information, as it is believed that driver’s decision on lane changing is made consciously based on those information. However, current research has shown that the usage of such surrounding information leads to high false alarm rate of lane-changing predict system [1]. Therefore this paper contributes to developing a lane-changing prediction method which uses vehicle state information only. From the perspective of the observer’s daily experience, this paper selects vehicle’s lateral trajectory and the spectrum of its lateral trajectory as input to predict drivers’ lane-changing intention. A Direction Convolutional LSTM (DCLSTM) network has been developed to predict drivers’ lane-changing behaviours. Recent pure LSTM methods proposed by researchers provide high accuracy when predicting the generation of drivers’ lane-changing intentions, but they have relatively low accuracy in predicting drivers’ lane-changing direction. DCLSTM retains pure LSTM network’s high accuracy in the prediction of drivers’ lane-changing intentions, while its prediction of drivers’ lane-changing directions is also accurate. All the training and testing data are extracted from the NGSIM dataset.

A self‐learning human‐machine cooperative control method based on driver intention recognition

Driver lane change intention recognition in the connected environment

In order to make Intelligent Transportation System (ITS) work effectively, a driver intention recognition method is proposed. In this research, three different recognition models were developed based on Continuous Hidden Markov Model (CHMM), and could distinguish left and right lane change intention from normal lane keeping intention. Subjects performed lane change maneuvers and lane keeping maneuvers with driving simulator which simulated highway scenes, parameters that highly correlated with lane change behavior were collected and analyzed. A series of testings and comparisons were done to obtain the optimal model structure and feature set. Results show that, taking the steering wheel angel, steering wheel angle velocity and lateral acceleration as the optimal observation signals, the accuracy can achieve up 95%, and it proved very effective in terms of early intention recognition.

Accurate identification of lane-changing intention can effectively assist intelligent driving vehicles in terms of decision-making and trajectory planning, which plays a significant role in enhancing driving safety by reducing traffic accidents caused by lane-changing. Based on the trajectory characteristics and vehicle interaction information, an attention-enhanced bidirectional multi-layer residual long-short term memory neural network (Attention Enhanced Residual-MBi-LSTM) model is proposed for lane change intention recognition in this paper. Firstly, an EWMA filter is employed to smooth the noisy data collected from the vehicle. Then a four-layer bidirectional residual LSTM memory (Residual-MBi-LSTM) structure is used to extract lane-changing features from the historical driving trajectories of ego-vehicle and vehicle interaction information. Besides, the attention mechanism is added to adjust the weight of data in different time frames. After that, the current lane-changing probability is calculated and output by the Softmax function. Finally, the vehicle lane-changing intention recognition model is firstly trained and then verified in the HighD dataset. According to the HIL experiment, the proposed model has the ability to identify driver intention on average of 2.07 seconds in advance.

By solving driver’s optimal handling input, this paper presents a novel Lane Changing Assistance System (LCAS) which can provide guidance for driver’s lane changing behavior. In addition, vehicle handling inverse dynamics method is proposed to solve driver’s optimal handling input. Firstly, to recognize driver’s lane changing intention and decrease the false alarm rate of LCAS, a lane changing intention recognition model is established. Secondly, the handling inverse dynamics model is established; and then the inverse dynamics problem is converted into the optimal control problem. Finally, the optimal control problem is converted into a nonlinear programming problem based on GPM; then sequential quadratic programming (SQP) is applied to get the solution. The direct collocation method (DCM) is used as the contrast verification of GPM. The simulation results show that the driver’s optimal handling input can be obtained according to driver’s lane changing intention in the proposed LCAS; and GPM has higher computational accuracy compared with DCM. This method may provide a reference for the research of LCAS and unmanned vehicles.

An intention identification method is proposed in this paper, which is based on vehicle running state and driving behavior interaction. The driving simulator experiment is designed to obtain vehicle driving time series data of lane keeping and lane changing. Based on the stage division of lane changing process, the lane changing intention stage can be obtained. It is found that the length of lane changing intention stage approximately obeys the normal distribution, and the length of left lane changing intention stage is greater than that of right lane changing. When exploring the influencing factors on the length of lane changing intention stage, it is found that the length of intention stage is approximately linearly correlated with the average speed of vehicles. Finally, based on the vehicle running state and intention stage, a continuous hidden Markov model based on Gaussian probability density function is established, which can effectively identify the driver's lane changing intention with high recognition accuracy and great real-time performance. The average accuracy of the model is 90%, the advanced time of left and right lane changing intention is 1.5s and 1.4s.

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of vehicles that pose a risk to it. However, due to the uncertainty of human intentions, accurately predicting lane change intentions and trajectories is a great challenge. Therefore, this paper aims to establish the connection between intentions and trajectories and propose a dual Transformer model for the target vehicle. The dual Transformer model contains a lane change intention prediction model and a trajectory prediction model. The lane change intention prediction model is able to extract social correlations in terms of vehicle states and outputs an intention probability vector. The trajectory prediction model fuses the intention probability vector, which enables it to obtain prior knowledge. For the intention prediction model, the accuracy can be improved by designing the multi-head attention. For the trajectory prediction model, the performance can be optimized by incorporating intention probability vectors and adding the LSTM. Verified on NGSIM and highD datasets, the experimental results show that this model has encouraging accuracy. Compared with the model without intention probability vectors, the impact of the model on NGSIM dataset and highD dataset in RMSE is improved by 57.27% and 58.70% respectively. Compared with two existed models, evaluation metrics of the intention prediction can be improved by 7.40-10.09% on NGSIM dataset and 2.17-2.69% on highD dataset within advanced prediction time 1s. This method provides the insights for designing advanced perceptual systems for autonomous vehicles.

To assure a safe, comfortable and especially a cooperative driving experience while driving semi, highly or even fully automated, anticipation of the driving behavior of other traffic participants is needed. Because of the amount of different traffic situations and influence factors on the task of driving, due to uncertainties in environmental sensor measurements, and as a consequence of variable and individual driving styles, probabilistic models in combination with machine learning techniques are often applied to learn driving behavior from data. In this paper, with the help of a simulator study, the driving behavior of a subject group is examined regarding their intention to change lane on highways. The simulator is set up as a partially automated driving system that takes discrete maneuver wishes as input (lane change left or lane change right). If the traffic situation permits it, the requested maneuvers is executed automatically by the system. This generates ground truth labels that are being used to train a lane change intention classifier. The results show that the approach is able to predict upcoming lane changes at an average of more than 3.5 seconds in advance.

The intention forecasting of the human driver plays an essential role in improving the safety and driver-machine coordination of the co-piloting vehicle, which is the decision-making foundation for driver assistance system (DAS). However, the quantitative prediction of the lane change decision, which determines the occasion and shape of the lane change path, has drawn little attention by the prior works. In this paper, a novel convolutional neural network (CNN)-based predictor that forecasts the driver’s lane change path is proposed. By including the driver’s eye-gazing and head-heading information, an earlier and more accurate prediction of driver’s time to lane change (TLC) and lane change duration (LCD), and we use the positive TLC and negative TLC to represent the classification results LC and RC, respectively. The classification and regression results validated that the inclusion of driver’s gazing information achieved an earlier and more precise detection of driver’s lane change intention. Furthermore, owning to the hierarchical structure of the CNN model and the parallel computing devices, the execution time of the proposed model is shorter and exhibits its powerful real-time computing performance. The estimated TLC and LCD are adopted to predict the driver’s target lane change trajectory with satisfactory accuracy. Therefore, the proposed model enables the DAS to generate a human-like lane change decision to minimize the driver-machine conflicts during lane change.

In order to quickly and accurately identify the lane changing intention of vehicles, and to deeply consider the time series characteristics of vehicle driving processes and the interactive effects between vehicles, a lane changing intention recognition model, namely, Model_TA, was constructed by combining the time series feature extraction ability of the encoder in the Transformer model, the bidirectional gating mechanism of the bidirectional gated recurrent unit, and the additive attention mechanism. The performance of the Model_TA model was trained and validated on the I-80 dataset in NGSIM. The experimental results showed that the accuracy of model intent recognition was 97.01%, which was 20.3%, 4.73%, and 1.73% higher than that of SVM, LSTM, and Transformer models, respectively; the prediction accuracy at 2.0 s, 2.5 s, and 3.0 s is 90.15%, 84.58%, and 83.13%, respectively, which is better than similar models. It is proved that the model can better predict the lane changing intention of vehicles.

For fully automated driving in the real-world, safety, comfort, and reliability are the key essentials during operation. However, in the automotive industry, it is critical to identify the lane change intention of vehicles from the adjacent lane for safe driving. Thus, the intelligent assistance system should be smart enough to track such lane change desires and assist the driver to avoid any accidents that is caused by human errors on the road. Here, in this paper we propose to predict the driving behaviors of lane-changing vehicles on the highways by studying their chronology. Machine learning performs well in retaining the human-vehicle behavioral patterns and therefore, we involved random forest to do this prediction. We proposed new feature space for lane change prediction, where a really small window size of 3-seconds can lead to high-performance. The extensive experiments are carried out on real-traffic dataset from Next Generation SIMulation (NGSIM) where we set up a new state-of-the-art accuracy of 98.6%.

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver's lane change intention. By using a deep learning-based method to model a driver's decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver's intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.

In this study, we are interested in brain-machine interfaces that extract movement related cortical potentials (MRCP) from an electroencephalogram (EEG) recorded while driving and use this information to classify/detect when a driver intends to do left or right lane change. Collecting EEGs while driving, however, is a challenging task as it introduces numerous artifacts, including head movements (e.g., to look at side mirrors, etc), eye movements, and whole-body movements. Such artifacts can dominate the signal, thus hampering MRCP and lane change intent detection. Here, we explore three EEG artifact removal algorithms tailored for MRCP detection while driving, namely: headset accelerometer-based independent component analysis (acc-ICA), constrained ICA (cICA) and empirical mode decomposition (EMD). Next, we propose to use a recurrent neural network (RNN) classifier for lane change intent detection and compare results with a conventional support vector machine (SVM) based classifiers. Lastly, we explore the effect of EEG window analysis size on artifact removal and classification performance, thus gauging how close to real-time enhancement can be achieved and with what delay can lane change intent be detected. When looking at averaged trial performance, all system combinations achieved reliable accuracy. On the other hand, for single-trial accuracy, only acc-ICA and EMD methods performed well. Overall, average classification accuracy of 54.25% was achieved with SVMs, whereas accuracies of 82.88%, 82.76% and 82.69% were achieved with the RNN using acc-ICA, cICA and EMD artifact removal algorithms, respectively. Window sizes of 4 seconds prior to lane changes achieved the best result and only six EEG electrodes were required.

The accelerated progression of urbanisation has emerged as a pivotal factor in the escalating issue of global warming, with automobile exhaust emissions assuming a central role in this context. This phenomenon underscores the growing prominence of energy consumption and environmental pollution as critical concerns. To promote the healthy development of green and sustainable transportation systems, this study analyses the impact of lane change intention (LCI) on reducing pollution and carbon in mixed traffic flow. The study proposes a traffic energy consumption model to analyse the impact of LCI on vehicle energy consumption in a mixed traffic flow environment. The model combines the collaborative adaptive cruise control (CACC) strategy to explore energy consumption in a multidimensional mixed traffic environment. The results showed that vehicles with LCI in mixed traffic flow had an average energy consumption increase of 7.65% compared to vehicles driving normally. When the vehicle adopted CACC, it could effectively alleviate the increase in energy consumption caused by LCI. Vehicles with the LCI and applying CACC only increased their energy consumption by an average of 4.84%, a decrease of 2.81% compared to those without cruise control. In summary, the research on pollution reduction and carbon reduction in mixed traffic flow environments under the influence of LCI for energy consumption analysis provides support and reference for the sustainable development of green transportation.