Syntactic pattern recognition of car driving behavior detection

Driver behavior remains a leading factor in road accidents, yet existing monitoring systems typically rely on single data modalities such as facial expressions or speech alone, limiting their reliability and contextual awareness. This work proposes a comprehensive driver behavior monitoring system using multimodal AI, uniquely integrating video, audio, and vehicle speed telemetry — an approach that remains underexplored in existing literature — to predict driver emotions and behaviors in real time. The system analyzes facial cues to detect visual anomalies, processes audio inputs to infer emotional states, and incorporates speed telemetry to provide additional behavioral context. This fusion of modalities is designed to improve classification accuracy and reduce false positives compared to unimodal approaches. Performance evaluation is conducted using benchmark datasets for both video-based and audio-based emotion recognition, with comparative analysis between individual and combined modalities. By addressing the challenges of multimodal integration and real-time processing, this research contributes a novel and effective framework for intelligent driver assistance systems, advancing the goal of enhanced road safety through predictive behavioral intervention. Additionally, this research is being extended to incorporate an intermediate-fusion-based multimodal decision framework, wherein top predictions from image, audio, and vehicle telemetry are jointly fed into a decision model (e.g., Random Forest or SVM) for improved context-aware warning generation. This approach addresses prior limitations of ensemble-style fusion and better aligns with the goals of true multimodal AI.

Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers’ EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development.

Automobiles have become one of the necessities of modern life, but also introduced numerous traffic accidents that threaten drivers and other road users. Most state-of-the-art safety systems are passively triggered, reacting to dangerous road conditions or driving behaviors only after they happen and are observed, which greatly limits the last chances for collision avoidances. Therefore, timely tracking and predicting the driving behaviors calls for a more direct interface beyond the traditional steering wheel/brake/gas pedal. In this paper, we argue that a driver's eyes are the interface, as it is the first and the essential window that gathers external information during driving. Our experiments suggest that a driver's gaze patterns appear prior to and correlate with the driving behaviors for driving behavior prediction. We accordingly propose GazMon, an active driving behavior monitoring and prediction framework for driving assistance applications. GazMon extracts the gaze information through a front-camera and analyzes the facial features, including facial landmarks, head pose, and iris centers, through a carefully constructed deep learning architecture. Our on-road experiments demonstrate the superiority of our GazMon on predicting driving behaviors. It is also readily deployable using RGB cameras and allows reuse of existing smartphones towards more safely driving.

<div class="section abstract"><div class="htmlview paragraph">The automatic lane change assist system is an intelligent driving assistance technology oriented to traffic safety, which requires trajectory planning of the lane change maneuver based on the lane change decision. A typical scene of lane change for overtaking is selected, where the front vehicle in the same lane and the rear vehicle in the left lane are deemed to be potential dangerous vehicles through the lane change. Lane change trajectory equation is first established according to the general law of steering wheel angle through lane changes. Based on the relative position, velocity and acceleration information of the dangerous vehicles and the lane change vehicle, motions of these surrounding dangerous vehicles are predicted. At the same time, a multi-objective optimization function is established based on the relative longitudinal safety boundary. The objectives are the minimum safety distance, the lane change time and the front wheel angle. The trajectory planning algorithm of the lane change assist based on the model predictive control with multi-objective is then studied. The influence of vehicle speed on trajectory tracking is analyzed in this paper, then the feasibility and robustness of the algorithm is verified. In addition, the complexity of the model is simplified in considering of real-time demand while accuracy is still guaranteed. Simulation and experimental results show that the trajectory planning algorithm based on the model predictive control with multi - objective has greater improvements of those objectives of lane change compared with PID control. The proposed trajectory optimization algorithm can provide reference for the automatic lane change assist system.</div></div>

Road accidents have become a common challenge in Tanzania’s urban centers, particularly Dar es Salaam, which records the highest number of traffic incidents nationally. This study examined the factors influencing the prevalence of road accidents in Tanzania, focusing on driver behaviors, motor vehicle faults, and pedestrian behaviors. Guided by a positivist philosophy and deductive approach, an explanatory research design was employed to establish causal relationships. A sample of 392 respondents, including drivers, conductors, passengers, and pedestrians, was selected using stratified and probability sampling techniques. Data were collected through structured questionnaires and documentary reviews from sources such as LATRA and the National Bureau of Statistics. Quantitative analysis was conducted using IBM SPSS, applying descriptive statistics and multiple regression analysis. Results revealed that driver behaviors, pedestrian behaviors, and vehicle faults all contribute to road accidents. However, pedestrian behaviors (β = 0.217, p = 0.000) and driver behaviors (β = 0.146, p = 0.044) showed the strongest significant influence, while vehicle faults had a weak and statistically insignificant effect (β = 0.109, p = 0.046). The study recommends enhancing driver education, promoting pedestrian safety through better infrastructure, and strengthening vehicle inspection programs. The findings provide empirical evidence for policymakers to design effective interventions to reduce road accidents in Tanzania.

Recent advances in driver behavior analysis for active safety have led to the ability to reliably predict certain driver intentions. Specifically, researchers have developed advanced driver assistance systems that produce an estimate of a driver's intention to change lanes, make an intersection turn, or brake, several seconds before the act itself. One integral feature in these systems is the analysis of driver visual search prior to a maneuver, using head pose and eye gaze as a proxy to determine focus of attention. However it is not clear whether visual distractions during a goal-oriented visual search could change the driver's behavior and thereby cause a degradation in the performance of the behavior analysis systems. In this paper we aim to determine whether it is feasible to use computer vision to determine whether a driver's visual search was affected by an external stimulus. A holistic ethnographic driving dataset is used as a basis to generate a motion-based visual saliency map of the scene. This map is correlated with predetermined eye gaze data in situations where a driver intends to change lanes. Results demonstrate the capability of this methodology to improve driver attention and behavior estimation, as well as intent prediction.

Lane changing maneuvers could have a substantial influence on traffic flow characteristics as a result of their interfering effect on surrounding vehicles. The interference effect of lane changing is more pronounced when heavy vehicles change lanes compared to when passenger cars undertake the same maneuver. This is due to the physical effects that the heavy vehicles impose on surrounding traffic. This paper investigates and compares the traffic flow characteristics which influence the lane changing behavior of heavy vehicle and passenger car drivers on freeways under heavy traffic conditions. A trajectory data set comprising 28 heavy vehicle and 28 passenger car lane changing maneuvers is analyzed in this study. The results suggest a substantial difference exists between the traffic characteristics influencing the lane changing behavior of heavy vehicle and passenger car drivers. Heavy vehicles' speed changes little during a lane changing maneuver. Heavy vehicle drivers mainly move into the slower lanes to prevent obstructing the fast moving vehicles which approach from the rear. However, passenger car drivers increase their speed according to the speeds of the lead and lag vehicles in the target lane. They more commonly move into the faster lanes to gain speed advantages.

Driver behavioral cues may present a rich source of information and feedback for future intelligent driver assistance systems (IDAS). Two of the most useful cues might be eye gaze and head motion. Eye gaze provides a more accurate proxy than head motion for determining driver attention, whereas the measurement of head motion head motion as a derivative of pose is less cumbersome and more reliable in harsh driving conditions. With the design of a simple and robust IDAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. We use a lane change intent prediction system [1] to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane change maneuver at a particular point in the future. Quantitative results using real-world data are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane change intent prediction. The addition of eye gaze does not improve performance as much as simpler head pose-based cues.

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Attempt of FRAM description integrating with Situation Awareness on a Car Driver's Behavior

More than 90% of traffic accidents are caused by driver behavior, with lane change behavior being a major contributor. Recently, driving assistance systems are being introduced on vehicles to reduce traffic accidents, and a reliable vehicle lane change collision detection system is a key component of these systems. Besides, the foundation of the vehicle lane change detection system is the effective vehicle lane change detection model. In this paper, based on the support vector machine, we propose a model for detecting driver lane change maneuvers and take into account the real-time vehicle dynamic features transmitted via Vehicle to X (V2X) Communication. The accuracy is ideal for lane keep and lane change situations, and it is also robust for zigzag driving situations, according to tests conducted using the NGSIM real traffic dataset. The detection accuracy for left and right lane change maneuvers is 97.5% and 99.09%, respectively, while the false alarm rate is 8.56%. Additionally, the average advance detection time is 1.7 s, which is suitable for actual driving application scenarios.

This paper deals with the development of Human-Centric Intelligent Driver Assistance Systems. Rear-end collisions account for a large portion of traffic accidents. To help mitigate this problem, predictive braking systems and adaptive cruise control systems have been developed. However, these types of systems usually rely solely on the vehicle and vehicle surround sensors, either ignoring the human component of driving or learning the driver's control behavior using only these sensors. As with all human-computer interfaces, this has the potential to work against the driver, distract the driver further, or even annoy the driver so that the driver ignores or disables the system. It is, therefore, important to directly take the driver's intended actions into account when designing a driver assistance system. By using a probabilistic model for the system, warnings and preventative measures can be constructed based on varying levels of situational severity and driver attentiveness and intent. The research is based upon carefully conducted experimental trials involving a human subjects driving in natural manner and on typical freeways in the USA. The experiments, designed by inputs from cognitive scientist, were conducted in a specially designed instrumented vehicle to record important cues associated with driver's behavior, vehicle state, and vehicle surround in a synchronized manner. Quantitative results and analysis of the experimental trials are presented to show the feasibility and promise of this framework to predict the driver's intent to brake, the need for braking given the current situation, and at what level the driver should be warned

Lane change safety assessment of coaches in naturalistic driving state

If the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human‐like lane‐changing model of an autonomous vehicle. The lane‐changing characteristic data of skilled drivers are collected and analyzed through a real vehicle test. Then, comparing the MPC‐based driver model with the steering wheel angle of human drivers, we found that the MPC‐based model could hardly reflect the maneuvering characteristics of human drivers, so we proposed a driver model with steering wheel angle continuity for human drivers. This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. These three test results show that the prediction results of the human‐like lane‐changing driving model based on Bi‐LSTM are closest to the real steering wheel angle sequence of skilled drivers. The test results demonstrate that the Bi‐LSTM‐based human‐like lane‐changing driving model achieves 9.8% RMSE and 6.8% MAE, which improves 10.8% RMSE and 10.3% MAE over LSTM. The model can generate the steering wheel angle sequence in the process of lane changing like a human, so as to realize the human simulation control of an autonomous vehicle for lane‐changing conditions.

We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework.