Driver's Intention Research Articles

Purchasing Intentions Analysis of Hybrid Cars Using Random Forest Classifier and Deep Learning

In developed or first-world countries, hybrid cars are widely utilized and essential in technological development and reducing carbon emissions. Despite that, developing or third-world countries such as the Philippines have not yet fully adopted hybrid cars as a means of transportation. Hence, the Sustainability Theory of Planned Behavior (STPB) was developed and integrated with the UTAUT2 framework to predict the factors affecting the purchasing intentions of Filipino drivers toward hybrid cars. The study gathered 1048 valid responses using convenience and snowball sampling to holistically measure user acceptance through twelve latent variables. Machine Learning Algorithm (MLA) tools such as the Decision Tree (DT), Random Forest Classifier (RFC), and Deep Learning Neural Network (DLNN) were utilized to anticipate consumer behavior. The final results from RFC showed an accuracy of 94% and DLNN with an accuracy of 96.60%, which were able to prove the prediction of significant latent factors. Perceived Environmental Concerns (PENCs), Attitude (AT), Perceived Behavioral Control (PBC), and Performance Expectancy (PE) were observed to be the highest factors. This study is one of the first extensive studies utilizing the MLA approach to predict Filipino drivers’ tendency to acquire hybrid vehicles. The study’s results can be adapted by automakers or car companies for devising initiatives, tactics, and advertisements to promote the viability and utility of hybrid vehicles in the Philippines. Since all the factors were proven significant, future investigations can assess not only the behavioral component but also the sustainability aspect of an individual using the STPB framework.

Vehicle motion trajectory prediction fusion algorithm with driver adventurousness correction factor based on CS-LSTM

Predicting the trajectories of adjacent vehicles plays an important role in the driving safety of adaptive cruise control system. It affects the safety and stability of the vehicle following the target vehicle during the vehicle cruising driving vehicle. However, due to the uncertainty of vehicle dynamics, driver character, and the complexity of the surrounding environment, vehicle trajectory prediction faces great challenges. Hence, a dynamic vehicle trajectory prediction system is proposed based on identifying driver intentions. First, based on a convolution LSTM, the driver adventurousness factor is introduced to describe the driver’s lane-change behavior heterogeneity and improve the accuracy of long-term lane-change trajectory prediction of adjacent lane vehicles. Second, the trajectory prototype predicted trajectory is updated by adjusting the minimum value function until the vehicle model corresponds to the planned sampling trajectory to improve the accuracy of the adjacent lane vehicle’s short-term lane-change trajectory prediction. Finally, the trajectories are fused using the trigonometric fusion algorithm, and the optimal trajectory is the output. The suggested strategy can predict lane-change intentions 2–5 s in advance. The prediction accuracy of the lane-change trajectory was approximately 21% higher than the normal prediction outcomes. The proposed method can be used to improve passenger comfort and the stability of a vehicle following a target vehicle that is separated from the adjacent lane vehicle.

Shared control based on ANFIS for brain-controlled driving

Using electroencephalography (EEG) signals to drive a vehicle could help disabled people expand their range of motion and improve their independence. A brain-controlled vehicle (BCV) is a vehicle that is commanded by analyzing EEG signals. However, the analysis and transmission effect of EEG signals is not ideal, the driving performance of the BCV solely relying on EEG signals is relatively poor. In this paper, to solve this problem, we propose a dynamic shared control method based on adaptive network-based fuzzy inference system (ANFIS). First, an ANFIS intelligent controller is designed to automatically make decisions according to the state of the vehicle. Then, safety coefficient and intention coefficient are proposed to evaluate the safety and driving intention of the brain-controlled driver. Finally, a fuzzy controller with safety and intention coefficients as inputs and brain-controlled driver weights as outputs is designed. The controller is the embodiment of a human–machine interaction, which allows the driver to maintain maximum control authority over the BCV under safe conditions by dynamically balancing the control authority of the brain-controlled driver and the ANFIS controller on the BCV. To verify the effectiveness of the proposed method, a joint simulation platform of Carsim and Matlab is established, and several groups of comparative simulation experiments are carried out, through which, it is demonstrated that the proposed method can effectively avoid road deviation while well maintaining the control authority of the brain-controlled driver.

EEG-based emergency braking intention detection during simulated driving

BackgroundCurrent research related to electroencephalogram (EEG)-based driver’s emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features.MethodsTo this end, a novel EEG-based driver’s emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input.ResultsWe recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking.ConclusionsThe study provides a user-centered framework for human–vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions.

Fail-Safe Behavior and Motion Planning Incorporating Shared Control for Potential Driver Intervention

Automated vehicles require an efficient fail-safe system for unexpected failures in normal operation. Although existing methods aiming at L4 driving automation leverage conservative motion planning to independently guide fail-safe behaviors when a normal planning function fails, they usually ignore human intervention demands. In fact, manual takeover is still a reliable choice of fail-safe maneuver today. Moreover, human drivers tend to handle critical situations, rather than trust the automated system with the vehicle control. Here, we propose a fail-safe architecture for vehicle behavior and motion planning module incorporating shared control in response to the driver intervention. The proposed method not only estimates driver intention to cooperate with the driver on maneuver selection, but also distributes the driving authority in accordance with the types of achievable minimal risk condition. By using the receding horizon planner with composition planning horizons, the driver is able to obtain an appropriate takeover condition on the basis of safety guarantee. Test in the library of critical scenarios shows that the proposed method is effective in terms of safety and assistance to the driver.

Triboelectric nanogenerator sensors for intelligent steering wheel aiming at automated driving

This paper reports a novel intelligent steering wheel developed based on the concept of triboelectricity aiming at automated driving to reduce traffic accidents. A sandwich-type sensor is designed to be integrated into the steering wheel with the aim of identifying driver’s steering intention. The steering wheel of a vehicle is furnished with a triboelectric nanogenerator (TENG)-based sensor for detecting driver intention. The superiority of the TENG-based sensor is demonstrated by comparing it to other available sensors within a vehicle. By employing different machine learning techniques, we develop classification models based on driving data from multiple drivers. We show that the faster reaction time of the TENG-based sensor can aid in emergency obstacle avoidance when compared to the regular steering wheel sensor through the use of model-predictive control. The fusion of data generated by the proposed TENG-based sensor and advanced control model represents a crucial step towards the development of an intelligent steering wheel for automated systems. This will improve the human–machine interaction for vehicle control, ultimately resulting in more efficient and effective control of the vehicle.

Intention Prediction-Based Control for Vehicle Platoon to Handle Driver Cut-In

Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.

DIPNet: Driver intention prediction for a safe takeover transition in autonomous vehicles

AbstractFollowing the successful development of advanced driver assistance systems (ADAS), the current research directions focus on highely automated vehicles aiming at reducing human driving tasks, and extending the operational design domain, while maintaining a higher level of safety. Currently, there are high research demands in academia and industry to predict driver intention and understating driver readiness, e.g. in response to a “take‐over request” when a transition from automated driving mode to human mode is needed. A driver intention prediction system can assess the driver's readiness for a safe takeover transition. In this study, a novel deep neural network framework is developed by adopting and adapting the DenseNet, long short‐term memory, attention, FlowNet2, and RAFT models to anticipate the diver maneuver intention. Using the public “Brain4Cars” dataset, the driver maneuver intention will be predicted up to 4 s in advance, before the commencement of the driver's action. The driver intention prediction is assessed based on 1) in‐cabin 2) out‐cabin (road) and 3) both in‐out cabin video data. Utilizing K‐fold cross‐validation, the performance of the model is evaluated using accuracy, precision, recall, and F1‐score metrics. The experiments show the proposed DIPNet model outperforms the state‐of‐the‐art in the majority of the driving scenarios.

Research on Behavior Recognition of Disyielding Pedestrians Based on LSTM

Serious traffic accidents caused by vehicles' disobedience to pedestrians occur frequently, so it is very important to supervise and regulate the behavior of disobedient pedestrians and explore the identification methods of disobedient behaviors. Compared with the existing research, this paper proposes a recognition algorithm based on Long Short Memory (LSTM) network for vehicles' indecisive pedestrian behavior. This algorithm is more practical and adaptable. First, it predicts the future continuous kinematics of the vehicle through the past transverse and longitudinal characteristics of the vehicle, and obtains the driver's manipulation intention. Then, the manipulation intention is combined with the past pedestrian trajectory and pedestrian head direction information, and the joint modeling is carried out to obtain the probability of the occurrence of vehicles' behavior that does not yield to pedestrians. Using real driving data to test, the test results show good recognition accuracy, and the goodness of fit of the model to the sample reaches 0.93.

Driver Intent Prediction and Collision Avoidance With Barrier Functions

For autonomous vehicles and driver assist systems, path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers. In the literature, the algorithms that provide driver intent belong to two categories: those that use physics based models with some type of filtering, and machine learning based approaches. In this paper we employ barrier functions (BF) to decide driver intent. BFs are typically used to prove safety by establishing forward invariance of an admissible set. Here, we decide if the “target” vehicle is violating one or more possibly fictitious (i.e., non-physical) barrier constraints determined based on the context provided by the road geometry. The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives. The predicted intent is then used by a control barrier function (CBF) based collision avoidance system to prevent unnecessary interventions, for either an autonomous or human-driven vehicle.

Comparative analysis of influencing factors on pedestrian road accidents

Road accident data includes detailed information about incidents that occurred, such as where they happened, the severity of the accident, and the number of people on the road at the time. Such information is useful in determining the causes of accidents and developing potential countermeasures. This research aims to determine the factors that contribute to pedestrian fatalities and injuries in traffic accidents. This study examined 150 pedestrian-vehicle accidents that took place between 1990 and 2021 in forty countries. Eleven factors have been identified as the major causes of accidents. The categorical principal component analysis (CATPCA) technique is used to reduce the number of dimensions and identify the elements that contribute to accidents. The eleven variables are classified into three groups: human factors, roadway environment, and vehicle attributes. The study found that car speed, weather, lighting, traffic conditions, area types, accident locations, and road conditions all had a significant impact on pedestrian accidents and fatalities. The findings show that a pedestrian's state (walking, running) and intention significantly increase the risk of serious injuries and death. The analysis of the driver's status suggests that the driver's intentions may also play a role in car accidents.

Haptics based multi-level collaborative steering control for automated driving

Increasing the capability of automated driving vehicles is motivated by environmental, productivity, and traffic safety benefits. But over-reliance on the automation system is known to cause accidents. The role of the driver cannot be underestimated as it will ultimately be the most relevant aspect for trust building and social acceptance of this technology. Here we introduce a driver-oriented automation strategy to achieve collaborative steering. Our approach relies on three major functionalities: interaction, arbitration, and inclusion. The proposed control strategy is grounded in the concept of shared control enabling driver intervention over the automation without deactivation. Well-defined physical human-robot interaction types are made available with the arbitration strategy. The automated driving trajectory is adapted to include the driver intent into the tactical level of trajectory planning. This enables driver initiated rerouting and consistent coordination of all vehicle actuators. In this way, automated vehicles, which rely on sight only, are augmented with the incorporation of the driver intent. The driver is neither replaced by nor excluded from the automation, rather their role remains active to the benefit of trust building and driving safety.

基于扩展计划行为理论的驾驶员跟驰意向分析

Road traffic flow will show the phenomenon of mixed driving of autonomous vehicles and traditional vehicles in the future. In order to explore psychological factors that affect traditional vehicle drivers' intention to follow autonomous vehicles in the human-machine hybrid driving environment, three variables of driver's understanding, risk perception, and acceptance of autonomous vehicles are introduced to construct an extended theoretical framework of planning behavior. Three hundred thirty-one subjective evaluation data are obtained through questionnaire survey. The internal consistency and reliability of the data are tested by SPSS and AMOS softwares. Finally, the path analysis and mediation effect analysis are carried out by using structural equation model to test relationship among the influencing factors. The results show that the structural equation model of driver's car-following intention based on the theory of extended planning behavior has good explanatory power for the driver's car-following intention in the mixed driving environment. Driver's behavioral attitude, subjective norm and perceived behavioral control of autonomous vehicles have a significant positive and direct effect on driver's car-following intention. Driver's risk perception and acceptance of autonomous vehicles have a significant indirect effect on driver's car-following intention through intermediary variables, in which the effect of risk perception is negative, and the effect of acceptance is positive. The degree of understanding has both a significant positive direct effect and a significant positive indirect effect on driver's car-following intention. The research results can be used as the basis for vehicle interaction behavior analysis in the human-machine hybrid driving environment.

Haptic Assistive Control With Learning-Based Driver Intent Recognition for Semi-Autonomous Vehicles

Semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. Further, the learning-based model has a 30.9% higher driver intent recognition accuracy than the finite state machine-based method. The holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer operation.

Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles

We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes (GPs) for predicting human driving behavior. The proposed approach employs the uncertainty about the GP's prediction to achieve safety. The multi-mode predictive control approach considers the possible intentions of the human drivers. While the intentions are represented by different Gaussian processes, their probabilities foreseen in the observed behaviors are determined by a suitable online classification. Intentions below a certain probability threshold are neglected to improve performance. The proposed multi-mode model predictive control approach with Gaussian process regression support enables probabilistic constraint satisfaction with high probability. The approach is underlined in simulation, considering real-world measurements for training the Gaussian processes.

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Driver behavior models have been used as input to self-coaching, accident prevention studies, and developing driver-assisting systems. In recent years, driver behavior recognition has revolutionized autonomous vehicles (AVs) and traffic management studies. This comprehensive survey provides an up-to-date review of the different driver behavior models and modeling approaches. In heterogeneous streets where humans and autonomous vehicles operate simultaneously, predicting the intent and action of human drivers is crucial for AVs with the help of wireless communication and artificial intelligence (AI) technologies. Therefore, the review also summarizes the applications of driver behavior modeling (DBM) for effective behavior recognition and human-like AV driving. Moreover, the review also covers the application of DBM in capturing behaviors of complex dynamic driving tasks. In this review, we solely cover car-following (CF) and lane-changing (LC) maneuvers.

Design and evaluation of a driver intent based mobile control interface for ground vehicles

Design and evaluation of a driver intent based mobile control interface for ground vehicles

Design and evaluation of a driver intent based mobile control interface for ground vehicles

Design and evaluation of a driver intent based mobile control interface for ground vehicles

Driven by head or heart? Testing the effect of rational and emotional anti-speeding messages on self-reported speeding intentions

With speeding being a major factor in road traffic injuries and deaths, an important challenge lies in devising effective policy measures to influence drivers' speeding intentions. This paper investigates the effects of rational and emotional anti-speeding messages on self-reported speeding intentions of drivers who received a speeding ticket. Message design and evaluation built on protection motivation theory (PMT). In a randomized 3-group survey (N = 600), Group A (n = 200) received a standard administrative message; Group B (n = 200) received the standard message together with a rational-based anti-speeding message; Group C (n = 200) received the standard message together with an emotional-based anti-speeding message. Only Group B respondents reported lower intention to speed in general. Both rational- and emotional-based messages influenced respondents' reported intentions to reduce their speed in school zones and residential areas (30 km/h speed limits in both instances). The standard administrative message accompanying a speeding ticket was not associated either with respondents' reported general or area-related speeding intentions. PMT provided a valid theoretical basis for developing and evaluating evidence-based anti-speeding messages.

Intention to provide ridesharing services: Determinants from the perspective of driver-partners in a gig economy

Research on ridesharing platforms under the gig economy has focused much on the incentives and barriers of users, leaving many gaps in understanding drivers’ intention to provide ridesharing services. This paper aims to explore, from the perspective of driver-partners, motives that encourage them to continue being gig workers. Data for the study are based on a cross-sectional survey of ridesharing drivers in three metropolitan areas in three regions (North, Central, and South) of Vietnam, conducted from June to July 2022. The paper regresses behavioral intention to continue being a gig driver on their demographic characteristics and self-estimation of economic benefit, time preference, and enjoyment of being a gig driver via ordered probit models. For all three regions, the result suggests that economic benefit, time preference, and enjoyment are good predictors of drivers’ intention to provide the services. Specifically, the probability of remaining in gig work among drivers decreases with their educational and economic status. Higher economic benefit does not predict a higher intention of drivers to stay longer in gig work. Similarly, those with higher levels of enjoyment of traveling and vehicles have a lower intention to remain in this sphere. In the North, the interaction terms between time preference and enjoyment level are significant, suggesting that the effect of enjoyment levels becomes less damaging with an increase in time preference. In other words, time preference is vital in keeping gig drivers in this type of work.