Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model

We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.

Spatial-Temporal ConvLSTM for Vehicle Driving Intention Prediction

Driving intention prediction from a bird’s-eye view has always been an active research area. However,existing research, on one hand, has only focused on predicting lane change intention in highway scenarios and, on the other hand, has not modeled the influence and spatiotemporal relationship of surrounding vehicles. This study extends the application scenarios to urban road scenarios. A spatial-temporal convolutional long short-term memory(ConvLSTM) model is proposed to predict the vehicle’s lateral and longitudinal driving intentions simultaneously. This network includes two modules: the first module mines the information of the target vehicle using the long short-term memory(LSTM) network and the second module uses ConvLSTM to capture the spatial interactions and temporal evolution of surrounding vehicles simultaneously when modeling the influence of surrounding vehicles. The model is trained and verified on a real road dataset, and the results show that the spatial-temporal ConvLSTM model is superior to the traditional LSTM in terms of accuracy, precision, and recall, which helps improve the prediction accuracy at different time horizons.

Torque Distribution Strategy of Electric Vehicle with In-wheel Motors Based on the Identification of Driving Intention

A driver’s intention is recognized accurately by employing fuzzy identification and a logic threshold including acceleration intention and steering intention.Different torque distribution control strategies are developed for different intentions and the driver’s torque demand is amended by fuzzy identification so that the response of the vehicle is more consistent with the driver’s intention of operation.Finally,a simulation model is built using MATLAB/Simulink to validate the control strategy.Simulation results show that the system accurately identifies the driver’s intention and improves the acceleration performance and steering stability of the vehicle.

A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving

In mixed and dynamic traffic environments,accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles to accomplish reasonable behavioral decisions and guarantee driving safety.In this paper,we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction,which consists of a driving inference model(DIM)and a trajectory prediction model(TPM).The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network.The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information.To further improve the prediction accuracy and realize uncertainty estimation,we develop a Gaussian process-based TPM,considering both the short-term prediction results of the vehicle model and the driving motion characteristics.Afterward,the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios.The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.

Optimization Transmission Efficiency with Driver Intention for Automotive Continuously Variable Transmission under Slip Mode

This study proposes and experimentally validates an optimal integrated system to control the automotive continuously variable transmission(CVT)by Model Predictive Control(MPC)to achieve its expected transmission efficiency range.The control system framework consists of top and bottom layers.In the top layer,a driving intention recognition system is designed on the basis of fuzzy control strategy to determine the relationship between the driver intention and CVT target ratio at the corresponding time.In the bottom layer,a new slip state dynamic equation is obtained considering slip characteristics and its related constraints,and a clamping force bench is established.Innovatively,a joint controller based on model predictive control(MPC)is designed taking internal combustion engine torque and slip between the metal belt and pulley as optimization dual targets.A cycle is attained by solving the optimization target to achieve optimum engine torque and the input slip in real-time.Moreover,the new controller provides good robustness.Finally,performance is tested by actual CVT vehicles.Results show that compared with traditional control,the proposed control improves vehicle transmission efficiency by approximately 9.12%-9.35%with high accuracy.

A Hierarchical LSTM-Based Vehicle Trajectory Prediction Method Considering Interaction Information

Trajectory prediction is an essential component in autonomous driving systems,as it can forecast the future movements of surrounding vehicles,thereby enhancing the decision-making and planning capabilities of autonomous driving systems.Traditional models relying on constant acceleration and constant velocity often experience a reduction in prediction accu-racy as the forecasted timeframe extends.This limitation makes it challenging to meet the demands for medium to long-term trajectory prediction.Conversely,data-driven models,particularly those based on Long Short-Term Memory(LSTM)neural networks,have demonstrated superior performance in medium to long-term trajectory prediction.Therefore,this study introduces a hierarchical LSTM-based method for vehicle trajectory prediction.Considering the difficulty of using a single LSTM model to predict trajectories for all driving intentions,the trajectory prediction task is decomposed into three sequential steps:driving intention prediction,lane change time prediction,and trajectory prediction.Furthermore,given that the driving intent and trajectory of a vehicle are always subject to the influence of the surrounding traffic flow,the predictive model proposed in this paper incorporates the interactional information of neighboring vehicle movements into the model input.The proposed method is trained and validated on the real vehicle trajectory dataset Next Generation Simulation.The results show that the proposed hierarchical LSTM method has a lower prediction error compared to the integral LSTM model.

Study on driver’s braking intention identification based on functional near-infrared spectroscopy

Purpose–Cooperative driving refers to a notion that intelligent system sharing controlling with human driver and completing driving task together.One of the key technologies is that the intelligent system can identify the driver’s driving intention in real time to implement consistent driving decisions.The purpose of this study is to establish a driver intention prediction model.Design/methodology/approach–The authors used the NIRx device to measure the cerebral cortex activities for identifying the driver’s braking intention.The experiment was carried out in a virtual reality environment.During the experiment,the driving simulator recorded the driving data and the functional near-infrared spectroscopy(fNIRS)device recorded the changes in hemoglobin concentration in the cerebral cortex.After the experiment,the driver’s braking intention identification model was established through the principal component analysis and back propagation neural network.Findings–The research results showed that the accuracy of the model established in this paper was 80.39 per cent.And,the model could identify the driver’s braking intent prior to his braking operation.Research limitations/implications–The limitation of this study was that the experimental environment was ideal and did not consider the surrounding traffic.At the same time,other actions of the driver were not taken into account when establishing the braking intention recognition model.Besides,the verification results obtained in this paper could only reflect the results of a few drivers’identification of braking intention.Practical implications–This study can be used as a reference for future research on driving intention through fNIRS,and it also has a positive effect on the research of brain-controlled driving.At the same time,it has developed new frontiers for intention recognition of cooperative driving.Social implications–This study explores new directions for future brain-controlled driving and wheelchairs.Originality/value–The driver’s driving intention was predicted through the fNIRS device for the first time.