Driver Lateral Control Research Articles

What Challenges Does the Full-Touch HMI Mode Bring to Driver's Lateral Control Ability? A Comparative Study Based on Real Roads

What Challenges Does the Full-Touch HMI Mode Bring to Driver's Lateral Control Ability? A Comparative Study Based on Real Roads

Data-driven human driver lateral control models for developing haptic-shared control advanced driver assist systems

In order to improve road safety, many advanced driver assist systems (ADAS) have been developed to support human-decision making and reduce driver workload. Currently, the majority of ADAS employ a single, often very simple, driver model to predict human-driver interaction in the immediate future (e.g., next few seconds). However, there is tremendous variability in how each individual drives, necessitating personalized driver models, based on data collected from observed actual driver actions. Yet, because we currently lack sufficient knowledge of the high-level cognitive brain functions, traditional control-theoretic driver models have difficulty accurately predicting driver actions. Recently, machine-learning algorithms have been utilized to predict future driver control actions. We compare several of these algorithms used to predict the lateral control actions of human drivers. Specifically, we compare these algorithms in terms of their suitability to develop haptic-shared ADAS, which share the control force with the human driver. To this end, we need to know how the steering torque is provided by the driver. However, low-cost driving simulators typically measure steering angle but not steering torque. Thus, this work also proposes a methodology to estimate the steering-wheel torque. Using the estimated steering torque, we train several machine learning driver control models and compare the performance using both simulated and real human-driving data sets.

Development of a Driver Lateral Control Model by Integrating Neuromuscular Dynamics Into the Queuing Network-Based Driver Model

This paper describes the development of a novel driver lateral control model by integrating the driver's neuromuscular dynamics into the queuing network (QN)-based driver lateral control model. Experimental results from 16 participants in a driving simulator show that, compared to the QN-based model, the proposed model performs better, and its performance is closer to that of drivers when a vehicle runs at a relatively high speed. The proposed model not only has the advantages of the models based on a cognitive architecture but also captures the dynamic interaction between the vehicular steering system and the driver's neuromuscular system. Thus, it can better represent driver lateral control and has greater value in supporting the development of driver assistance systems.

Using Queuing Network and Logistic Regression to Model Driving with a Visual Distraction Task

Computational dual-task models of driving with a secondary task can help compute, simulate, and predict driving behavior in dual task situations. These models can thus help improve the process of developing in-vehicle devices by reducing or eliminating the need for conducting driver experiments in the early stage of the development. Further, these models can help improve traffic flow simulation. This article develops a dual-task model of driving with a visual distraction task using the Queuing Network model of driver lateral control and a logistic regression model. The comparison between the model simulation data and the human data from drivers in a driving simulator shows that this computational model can perform driving with a secondary visual task well and its performance is consistent with the driver data.

Queuing Network Modeling of Driver Lateral Control With or Without a Cognitive Distraction Task

In this paper, we propose a computational model of driver lateral control based on the queuing network cognitive architecture and the driver preview model about driver lateral control activities. This computational model was applied to model the dual tasks of driving with a cognitive distraction task. The comparison between human driver data and model simulation data shows that this computational model can perform vehicle lateral control well, and its performance is consistent with that of drivers under single- and dual-task driving conditions. Furthermore, we examine the effectiveness of some parameters of the model in representing different styles of driving and discuss the value of this computational model in facilitating the evaluation of vehicle dynamics and driver assistant systems and providing new insights into research on unmanned vehicle control techniques.

Lateral control assistance in car driving: classification, review and future prospects

This study puts forward a classification of driver lateral control assistance devices based on distinctions among several cooperative activities between the driver and the assistance devices. The proposed classification is based on prior work by Hoc, Young and Blosseville and Young, Stanton and Harris, who put forward related theoretical frameworks on human–machine cooperation with automation. The particular application here to lateral control allows for a human-centred categorisation of existing and potential (i.e. near-future) driver assistance devices. Four human–machine cooperation levels based on drivers' activities have been adopted. All of the proposed categories are reviewed in three steps. First, each device category is functionally defined. Next, the impact of the devices on driving behaviour is presented. A third part sums up the effectiveness of each assistance category, particularly with regard to accident data. The general conclusion synthesises the main insights for each human–machine category proposed and highlights a number of design recommendations.

Mathematical Modeling of Average Driver Speed Control with the Integration of Queuing Network-Model Human Processor and Rule-Based Decision Field Theory

Quantitative prediction and understanding of driver speed control is important to prevent speeding behavior and design vehicle systems. Speed control is a complex behavior of driver longitudinal vehicle control, involving speed perception, decision making (setting a target speed), motor control (foot movement for pedal control), and vehicle mechanics. However, few of existing models is able to cover all of these important aspects together. To address this problem, the current work built a new mathematical driver speed control model with analytical solutions based on rigorous understanding of human cognitive mechanisms in driving, integrated Queuing Network-Model Human Processor (QN-MHP, which already modeled driver lateral control) structure and Rule-Based Decision Field Theory (RDFT), and offered a relatively complete picture of driver speed control in free-flow driving settings. This new model can provide predictions with regard to driving speed, pedal angle and acceleration for average driver.

The impact of verbal interaction on driver lateral control: an experimental assessment

Driver distraction is acknowledged as one of the key contributors to driver accidents (Treat, J.R., et al., 1977. Tri-level study of the causes of traffic accidents (No. DOT-HS-034‐535‐77-TAC(1)). Bloomington, IN: Institute for Research in Public Safety – Indiana University; Knipling, R.R., et al., 1993. Assessment of IVHS countermeasures for collision avoidance: Rear-end crashes (No. DOT HS 807 995). Washington, DC: National Highway Traffic Safety Administration). As driving is mainly considered a visual task (Wierwille, W.W., 1993. Visual and manual demands of in-car controls and displays. In: B. Peakock and W. Karwowski, eds. Automotive ergonomics. London: Taylor and Francis, 229–320) the use of auditory channels for interacting with intelligent vehicle systems has been suggested as a solution to possible visual overload. This article presents two studies which assess the potential impact of distraction caused by verbal interaction on the driving task. The first study used a low-cost, game-based, simulation and the second study used the same experimental design with a generic driving simulation, the Lane Change Task (Mattes, S., 2003. The lane change task as a tool for driver distraction evaluation. In: H. Strasser, H. Rascher, and H. Bubb, eds. Quality of work and products in enterprises of the future. Stuttgart: Ergonomia Verlag, 57–60). Twenty-four young adults, 12 males and 12 females, participated in the first study and 12 young adults, 6 males and 6 females, in the second study. Road departures, time/speed and subjective workload were the measures in the first study, while the second study used mean course-departure and subjective workload as dependent variables. The results indicated that game-based simulation can be a solution when realism is needed but resources are limited, and suggested that concurrent verbal interaction may impair lateral vehicle control.

Analysis of Crash Rates and Surrogate Events

A preliminary study was done into the use and validation of crash surrogates, which are obtained from naturalistic driving studies for the detailed analysis of risk factors. The approach is based on a unified statistical analysis of crash data and surrogate events that uses a spatial referencing system and a common measure of exposure. Statistical methods based on a bivariate response and Bayesian update models were adapted to the joint analysis of crashes and surrogates. The study specifically addresses road-departure crashes involving a single vehicle. It is proposed that suitable surrogates be based on underlying continuous measures of disturbance in the driver's lateral control of the vehicle. Naturalistic driving data from a field operational test conducted in southeastern Michigan were spatially joined with highway data and crash data from the same area, and a set of candidate crash surrogates was tested. Analysis results indicated that simple lateral lane position did not provide a satisfactory surrogate, whereas estimated time to road departure was found to show the correct statistical dependencies, consistent with the crash data. The approach developed in the study provided a way to assess crash risk in a common framework and also to validate or invalidate candidate surrogates. When applied to data from the future SHRP 2 naturalistic driving study, the increased statistical power resulting from the much larger data set will provide more definitive conclusions about surrogate validity and factors influencing overall crash risk.

Characterization of the Lateral Control Performance by Human Drivers on Highways

The characterization of human drivers' performance is of great significance for highway design, driver state monitoring, and the development of automotive active safety systems. Many earlier studies are restricted by experimental scope, the number and diversity of human subjects, and the accuracy and extent of measured variables. In this work, driver lateral control performance on limited-access highways is quantified by utilizing a comprehensive naturalistic driving database, with the emphasis on measures of vehicle lateral position and time to lane crossing (TLC). Normative values at various speed ranges are reported. The results represent a statistical view of baseline on-road naturalistic driving performance, and can be used for quantitative studies such as driver impairment and alertness monitoring, the triggering of lane departure warning systems, and highway design. © 2008 SAE International.