Intention Recognition Model Research Articles

An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle

Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential collision risk for following vehicle. Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. According to the recognized driver’s intention transmitted by Internet of vehicles, an AEB model for the following vehicle is proposed, which can dynamically change the critical braking distance under different driving conditions to avoid rear-end collision. In order to verify the performance of the proposed models, we conducted driver’s intention recognition and AEB simulation tests in the cosimulation environment of Simulink and PreScan. The simulation test results show that the average recognition accuracy of the proposed BP-HMM model was 98%, which was better than that of the BP and HMM models. In the Car to Car Rear moving (CCRm) and Car to Car Rear braking (CCRb) tests, the minimum relative distance between the following vehicle and the front vehicle was within the range of 1.5 m–2.7 m and 2.63 m–5.28 m, respectively. The proposed AEB model has better collision avoidance performance than the traditional AEB model and can adapt to individual drivers.

Transition characteristics of driver's intentions triggered by emotional evolution in two‐lane urban roads

Driver's intention is a self-internal state that represents a commitment to carrying out driving action at the next moment, which could be affected by driver's emotion. Therefore, understanding driver's emotion is an important basis for developing driver intention recognition models. This study aims to gain a better insight of the characteristics of driver intention transition trigged by driver's emotion. The Hidden Markov model was used to develop a driver intention recognition model with the involvement of driver's emotions. Assorted materials including visual, auditory and olfactory stimuli were used to evoke driver's emotions before the driving experiments, as well as keep and increase the emotional level during driving. Real and virtual driving experiments were conducted to collect human-vehicle-environment dynamic data in two-lane roads. The results show that the proposed model can achieve high accuracy and reliability in estimating driver's intention transition with the evolution of driver emotion. Our findings of this study can be used to develop the personalized driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance for improving road traffic safety.

Short-text intention recognition based on multi-dimensional dynamic word vectors

For short-texts with few words and sparse features, it becomes very important to use text context semantic information to enrich its text representation. At present, the text representation method of static word vectors is the same for the same word in different sentences. From a certain perspective, contextual semantic information is not fully utilized. Therefore, this paper proposes to use the combination of BERT and ELMo to mine more comprehensive contextual semantic information from different dimensions to enrich the text representation. The BERT model fully describes the characteristics of word level, sentence level and relationship between sentences. The ELMo model can solve the problem of polysemy. LSTM and Attention Mechanism are used to extract high-dimensional text features, and CRF is used to recognize intentions. Experiments show that the multi-dimensional dynamic word vector intention recognition model (MD-Intention) proposed in this paper has achieved good performance on both the ATIS-2 dataset and the SNIPS dataset.

Driver’s Intention Identification With the Involvement of Emotional Factors in Two-Lane Roads

Driver’s emotion is a psychological reaction to environmental stimulus. Driver intention is an internal state of mind, which directs the actions in the next moment during driving. Emotions usually have a strong influence on behavioral intentions. Therefore, emotion is an important factor that should be considered, to accurately identify driver’s intention. This study used the support vector machine (SVM) theory to develop a driver intention recognition model, with the involvement of driver’s emotions. Various materials including visual materials, auditory materials, and olfactory materials, were used to induce driver’s emotions. Real driving, virtual driving and computer simulation experiments were conducted to collect human-vehicle-environment dynamic data in two-lane roads. The results present that the proposed model can achieve high accuracy and reliability in recognizing driver’s intentions. Our findings of this study can be used to develop the personalized driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance for improving road traffic safety.

Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data.

Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles.

Comparison of the Pedestrian Crossing Intention Parameters and Research on Intention Recognition Model Under Different Road Conditions

There have a large number of pedestrian-vehicle accidents on the pedestrian crossing area in China every year, causing huge loss of life and property. In view of different road conditions, it's crucial to establish a more accurate crossing intention recognition model to improve the safety of pedestrians. In this work, a pedestrian crossing area was chosen. Due to construction reasons, two road conditions appeared in the same crossing area at different periods, namely a condition with a zebra crossing and that without a zebra crossing. We compared pedestrian crossing intention parameters under two road conditions in the same crossing area. The results found that there was a great difference in the characterization parameters of pedestrian crossing intention when the site with and without a zebra crossing. Additionally, a more comprehensive crossing intention characteristic parameters set was established. The characteristic parameters were pedestrian speed, the distance between vehicle and crossing area, time to collision (TTC), and safe vehicle deceleration (SVD), pedestrian age, pedestrian gender, group, respectively. The pedestrian intention recognition model for the site with a and without a zebra crossing were established by long short-term memory network integrated with the attention mechanism (AT-LSTM). When the model recognized pedestrian crossing intention 0.6 seconds in advance, the recognition accuracies were 93.05% and 93.89% respectively. The research results are of great significance for improving the safety of autonomous vehicles in the future, and there are also important to improve pedestrian safety.

SEMG Based Human Motion Intention Recognition

Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

Minimum Time Lane Changing Problem of Vehicle Handling Inverse Dynamics Considering the Driver’s Intention

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.

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.

Recognition of Combat Intention with Insufficient Expert Knowledge

Aiming at the problem that the relationship between targets’ state feature and combat intention cannot be quantified under the condition of insufficient expert knowledge, a combat intention recognition model based on deep neural network is designed. By introducing the ReLU activation function and the Adam optimization algorithm, the convergence speed of the model is improved, and the local optimization is effectively prevented. The experimental results show that the proposed method can effectively recognize the target's combat intention and obtain better recognition results.

An E-Commerce Customer Service Robot Based on Intention Recognition Model

There are three defects for providing human-labor customer services in e-commerce operations: high costs of human labors, staff turnover, and lack of service quality assurance. Breakthroughs made in artificial intelligence, natural language processing and related fields make it possible to replace human labors with online artificial intelligent robots to provide the e-commerce customer service, which indicates the online robots are the future of e-commerce customer services. However, most of the current robots are designed to reply with knowledge matching the key words in question sentences from the database, rarely involving in research on customer intentions that are key factors influencing user experience and online sales. In this research, an intention recognizing model was proposed to obtain intentions of e-commerce consumers by computing the strengths of candidate intention nodes in the intention graph, which was used to describe relations between different goods that could be the intentional targets of e-commerce consumers. The proposed robot was constructed based on the intention recognizing model to identify intentions of consumers and use the located knowledge combined with the AIML based sentence composition template to produce the response sentences for consumers. At last, the proposed robot was evaluated using F3 and ROUGE metrics by comparing with a keyword matching robot. And the evaluation results proved the validity of the proposed robot.

Braking Energy Recovery Research for Electric Vehicles

For the electro-hydraulic braking system in the electric vehicles, a coordinated control strategy of the motor braking and hydraulic one was proposed, which includs electric vehicle braking intention recognition model contains the brake pedal and the accelerator pedal. The simulation mode was built by using Cruise and Matlab/Simulink. The braking stabilities were simulated at different adhesion coefficient and braking intensity. The simulation results show that: Regenerative braking strategy for electric vehicles under braking energy can be recovered under different conditions, braking energy recovery rate in the early 100km / h speed low intensity braking conditions can reach 73.2%. And regenerative braking results validate the feasibility of the effectiveness of coordinated strategies to match the vehicle's power to improve the electric car electric-hydraulic brake energy recovery efficiency.