Design Of Driver Assistance Systems Research Articles

Relationship between braking intensity and driver heart rate as a function of the size of the measurement window and its position

Heart Rate (HR) is often used to capture the driver's mental workload and stress. Usually, during a predefined time window (e.g., 10 s, 30 s, or longer). In addition, the center of that time window is commonly placed at the peak of events of interest (e.g., safety events such as hard braking). However, the choices of window size and window position relative to driving events are often not explained in the literature, and conceivably, other choices could have been made for estimating changes in HR. This research tested the effect of window size and position on HR estimates. Participants executed 12 braking events on a closed driving course and were monitored for HR and skin conductivity. We fitted a regression model on HR as a function of driver braking intensity, multiple times (N = 210), each time, we calculated the HR with different window sizes (1–70 s) and three window positions (onset of braking, peak deceleration, and end of braking). We found that the coefficients in the model on HR by braking intensity changed as a function of the window size and its position. Our sample's optimal time window size was different from conventional settings: it was 4-second long and centered at the end of the braking events. Our findings showcase the analysis procedure that we employed and can contribute to research on physiological markers (HR and others) and to the design of driver assistance systems.

Multimodal Warnings Design for In-Vehicle Robots under Driving Safety Scenarios.

In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of multimodal warnings for in-vehicle robots under driving safety warning scenarios. Based on transparency theory, this study addressed the content and timing of visual and auditory modality warning outputs and discussed the effects of different robot speech and facial expressions on driving safety. Two rounds of experiments were conducted on a driving simulator to collect vehicle data, subjective data, and behavioral data. The results showed that driving safety and workload were optimal when the robot was designed to use negative expressions for the visual modality during the comprehension (SAT 2) phase and speech at a rate of 345 words/minute for the auditory modality during the comprehension (SAT 2) and prediction (SAT 3) phases. The design guideline obtained from the study provides a reference for the interaction design of driver assistance systems with robots as the interface.

Predictive Models of Driver Deceleration and Acceleration Responses to Lead Vehicle Cutting In and Out

A common maneuver drivers perform and experience on the road is changing lanes. Autonomous vehicles are required to engage a lane change safely and to react to the other road users’ lane changes. To develop autonomous vehicles that change lanes or respond to the lead vehicle’s lane changes in a safe and human-like way, one should investigate the factors that affect human driver responses. By reviewing the literature to identify potential factors, this study extracted these factors from a naturalistic driving data set and associated them with driver deceleration and acceleration responses to the lead vehicle’s cut-in and cut-out to develop predictive models for the impact of the events on traffic flow. After the events were verified as accurate, the variables associated with the events, including range, range rate, speed, lateral position in the lane, and average acceleration were analyzed using logistic regression, support vector machines (SVM), and two forms of decision trees. In total, 799 cut-in events and 684 cut-out events with the necessary variables were applied for analysis. The significant variables influencing driver behavior were found, and using these variables, the predictive models achieved around 80% accuracy for cut-ins, and 73% accuracy for cut-outs on test data. These results will assist in the future design of autonomous vehicle control to minimize detrimental effects on traffic when changing lanes and safe longitudinal control when responding to a lead vehicle’s lane changes, allowing for safe integration with human drivers, and better design of driver assistance systems.

Using a multi-user driving simulator system to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits

Vehicle fleet rear-end collisions (FRECs) are an extremely fatal type of traffic collisions on freeway and they usually occur in foggy weather. This study aimed to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits on freeway. A multi-user driving simulator system was used to conduct the experiment and the driving behavior data were collected from eight participants. The experimental results showed that as the fog density increased, the length of vehicle fleet decreased significantly, and drivers tended to keep a more stable car-following distance. The fog weather and short vehicle gap prompted drivers to react faster and brake harder in respond to the leading vehicle’s brake. In spite of the compensational behaviors, more FRECs were observed under heavy fog condition. Lowering speed limit can significantly reduce the FRECs under foggy conditions. As the speed limits reduced, drivers’ brake response time and speed variance significantly reduced. The study also found that drivers’ brake response time was negatively correlated with their positions in the fleet. Drivers in the front positions of the fleet had a longer response time than drivers in the back positions and thus were more likely to encounter collisions. The study generated a better understanding of drivers’ behavioral pattern in a vehicle fleet and the patterns of vehicle fleet rear-end collisions occurrence. The findings also shed lights on the design of driver assistance system for complex driving situations such as freeway driving under adverse weather.

Ultra-local model predictive control: A model-free approach and its application on automated vehicle trajectory tracking

Model predictive control (MPC) has been extensively utilized in the automotive applications, such as autonomous vehicle path planning and control, hybrid-vehicle energy management, and advanced driver-assistance system design. As a typical model-based control law, MPC relies on a system model to predict the state evolution of the manipulated plant within the prediction horizon. However, a representative yet concise mathematical description of the controlled plant may not always be available in practice. Therefore, model-free strategies, e.g., identification for control and direct data-driven control, have been incorporated into the predictive control framework. Nonetheless, existing model-free predictive controllers usually require reliable datasets and employ complex nonconvex optimizations to identify the underlying system model. Furthermore, their control performances are fundamentally limited by the quality of the training data. Inspired by the model-free control, this paper proposes the ultra-local model predictive control (ULMPC), which is a novel and straightforward model-free predictive control technique with no need for the computationally-extensive model learning process. The proposed ULMPC is implemented for automated vehicle trajectory following. Carsim-Simulink joint simulations and indoor experimental field tests with a scaled car demonstrate its effectiveness.

Checklist for Expert Evaluation of HMIs of Automated Vehicles—Discussions on Its Value and Adaptions of the Method within an Expert Workshop

Within a workshop on evaluation methods for automated vehicles (AVs) at the Driving Assessment 2019 symposium in Santa Fe; New Mexico, a heuristic evaluation methodology that aims at supporting the development of human–machine interfaces (HMIs) for AVs was presented. The goal of the workshop was to bring together members of the human factors community to discuss the method and to further promote the development of HMI guidelines and assessment methods for the design of HMIs of automated driving systems (ADSs). The workshop included hands-on experience of rented series production partially automated vehicles, the application of the heuristic assessment method using a checklist, and intensive discussions about possible revisions of the checklist and the method itself. The aim of the paper is to summarize the results of the workshop, which will be used to further improve the checklist method and make the process available to the scientific community. The participants all had previous experience in HMI design of driver assistance systems, as well as development and evaluation methods. They brought valuable ideas into the discussion with regard to the overall value of the tool against the background of the intended application, concrete improvements of the checklist (e.g., categorization of items; checklist items that are currently perceived as missing or redundant in the checklist), when in the design process the tool should be applied, and improvements for the usability of the checklist.

Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles.

Electric-vehicle technology is an emerging area offering several benefits such as economy due to low running costs. Electric vehicles can also help to significantly reduce CO2 emission, which is a vital factor for environmental pollution. Modern vehicles are equipped with driver-assistance systems that facilitate drivers by offloading some of the tasks a driver does while driving. Human beings are prone to errors. Therefore, accidents and fatalities can happen if the driver fails to perform a particular task within the deadline. In electric vehicles, the focus has always been to optimize the power and battery life, and thus, any additional hardware can affect their battery life significantly. In this paper, the design of driver-assistance systems has been introduced to automate and assist in some of the vital tasks, such as a braking system, in an optimized manner. We revamp the idea of the traditional driver-assistance system and propose a generic lightweight system based on the leading factors and their impact on accidents. We model tasks for these factors and simulate a low-cost driver-assistance system in a real-time context, where these scenarios are investigated and tasks schedulability is formally proved before deploying them in electric vehicles. The proposed driver-assistance system offers many advantages. It decreases the risk of accidents and monitors the safety of driving. If, at some point, the risk index is above a certain threshold, an automated control algorithm is triggered to reduce it by activating different actuators. At the same time, it is lightweight and does not require any dedicated hardware, which in turn has a significant advantage in terms of battery life. Results show that the proposed system not only is accurate but also has a very negligible effect on energy consumption and battery life.

Feedback Game-Based Shared Control Scheme Design for Emergency Collision Avoidance: A Fuzzy-Linear Quadratic Regulator Approach

Driver-machine shared control scheme opens up a new frontier for the design of driver assistance system, especially for improving active safety in emergency scenario. However, the driver's stress response to steering assistance and strong tire nonlinearity are main challenges suffered by controller designing for collision avoidance. These unfavorable factors are particularly pronounced during emergency steering maneuvers and sharply degrade shared control performance. This paper proposes a fuzzy-linear quadratic regulator (LQR) game-based control scheme to simultaneously enhance vehicle stability while compensating driver's inappropriate steering reaction in emergency avoidance. A piecewise linear-based Takagi–Sugeno (T–S) fuzzy structure is presented to mimic driver's knowledge about vehicle lateral nonlinearity, and the control authority is shared between driver and emergency steering assistance (ESA) system through steer-by-wire (SBW) assembly. An identical piecewise internal model is chosen for ESA and the shared lane-keeping problem is modeled as a fuzzy linear quadratic (LQ) problem, where the symmetrical fuzzy structure further enhances vehicle's ability to handle extreme driving conditions. In particular, the feedback Stackelberg equilibrium solutions of the fuzzy-LQ problem are derived to describe the interactive steering behavior of both agents, which enables the ESA to compensate driver's irrational steering reaction. Hardware-in-the-loop (HIL) experiment demonstrates the ESA's capability in compensating driver's aggressive steering behavior, as well as the copiloting system's excellent tracking and stabilizing performance in emergency collision avoidance.

Design of driver assistance system for air cushion vehicle with uncertainty based on model knowledge neural network

In this paper, considering the difficult maneuverability of the air cushion vehicle (ACV), a driver assistance system (DAS) of ACV including an intuitive human-computer interface, DAS monitor and DAS controller is developed for humans. The human-computer interface is easy to be understood and used for humans. And as DAS monitor, appropriate sensors installed at handles of rudders and propellers are used to monitor driver's operational changes. For the design of DAS controller, model knowledge neural network (MKNN) method is first proposed in this paper to deal with the parameter uncertainty of ACV's complex model. Then the MKNN-based controller is designed as the DAS controller. The DAS with MKNN-based controller can assist drivers in better control operations according to their action instructions. And numerical simulations are implemented to demonstrate the effectiveness and superiority of the developed DAS with MKNN-based controller.

A Study on Driver Assistance System Design Based on Sense of Self-Agency

A Study on Driver Assistance System Design Based on Sense of Self-Agency

Drivers’ Reliance on Lane-Keeping Assistance Systems as a Function of the Level of Assistance

We investigated drivers’ willingness to allocate control to a lane-keeping assistance system as a function of the level of assistance. Operators were found to increasingly rely on automation with higher reliability. We assume a similar effect of the level of assistance on reliance resulting from operators’ perception of a reduced need to engage in automation controlled processes. Forty five participants drove in a moving base driving simulator wiTheither a lane-keeping control (LKC) system (high level of assistance), a lane departure warning (LDW) system (low level of assistance) or without lane-keeping assistance. The allocation of visual attention away from the road scene towards an in-vehicle display of a secondary task was used as an indicator of drivers’ reliance on the systems. We found that drivers diff ered significantly in their reliance on and trust in the LKC system. Individual driver characteristics, i.e., self reported driving style, had a significant influence on drivers’ trust. Drivers placed more trust in the LKC system the more they regarded themselves as anxious and unconfident drivers, and more trust in the LDW system the more they regarded themselves as unobservant drivers. A major conclusion is that self-reported driving style is an important factor to predict if drivers are prone to place their trust in, and rely on, higher level driving assistance. Study results call for transparency in the design of driver assistance systems to avoid false expectations regarding system capabilities. Functional system limits should be made easily identifiable for drivers in order to reduce the likelihood of overreliance.

Surrogate safety measures as aid to driver assistance system design of the cognitive vehicle

The driver assistance system part of the cognitive vehicle design can prevent rear, lateral and other collisions by using a collision warning system that integrates intelligent technology and human factors. To be effective, such a system should be able to analyse driving states including driver distraction and driver intent, assess the likelihood of collisions by working with surrogate safety measures and issue warnings to the driver. This study presents a longitudinal and lateral collision warning model that allows the inclusion of key surrogate safety measures such as distance between vehicles in longitudinal vehicle-following mode or envelopes of vehicles in the lateral direction during lane migration/change/merge movements. The model can take into account values of driver distraction and driver intent variables obtained on-line or from off-line devices. The formulation is also applicable to time-to-crash surrogate safety measure. A pattern recognition method is used for the identification of pre-crash condition while minimising false alarms. The surrogate safety model is presented and illustrative examples are provided. The surrogate safety measure-based warning system is mainly intended for on-line use in actual driving conditions. In addition, it can be used in driving simulators or for off-line safety studies in association with microsimulators of traffic.

Designing driver assistance systems with crossmodal signals: multisensory integration rules for saccadic reaction times apply.

Modern driver assistance systems make increasing use of auditory and tactile signals in order to reduce the driver's visual information load. This entails potential crossmodal interaction effects that need to be taken into account in designing an optimal system. Here we show that saccadic reaction times to visual targets (cockpit or outside mirror), presented in a driving simulator environment and accompanied by auditory or tactile accessories, follow some well-known spatiotemporal rules of multisensory integration, usually found under confined laboratory conditions. Auditory nontargets speed up reaction time by about 80 ms. The effect tends to be maximal when the nontarget is presented 50 ms before the target and when target and nontarget are spatially coincident. The effect of a tactile nontarget (vibrating steering wheel) was less pronounced and not spatially specific. It is shown that the average reaction times are well-described by the stochastic “time window of integration” model for multisensory integration developed by the authors. This two-stage model postulates that crossmodal interaction occurs only if the peripheral processes from the different sensory modalities terminate within a fixed temporal interval, and that the amount of crossmodal interaction manifests itself in an increase or decrease of second stage processing time. A qualitative test is consistent with the model prediction that the probability of interaction, but not the amount of crossmodal interaction, depends on target–nontarget onset asynchrony. A quantitative model fit yields estimates of individual participants' parameters, including the size of the time window. Some consequences for the design of driver assistance systems are discussed.

Carryover effects of highly automated convoy driving on subsequent manual driving performance.

In the present study, we tested to what extent highly automated convoy driving involving small spacing ("platooning") may affect time headway (THW) and standard deviation of lateral position (SDLP) during subsequent manual driving. Although many previous studies have reported beneficial effects of automated driving, some research has also highlighted potential drawbacks, such as increased speed and reduced THW during the activation of semiautomated driving systems. Here, we rather focused on the question of whether switching from automated to manual driving may produce unwanted carryover effects on safety-relevant driving performance. We utilized a pre-post simulator design to measure THW and SDLP after highly automated driving and compared the data with those for a control group (manual driving throughout). Our data revealed that THW was reduced and SDLP increased after leaving the automation mode. A closer inspection of the data suggested that specifically the effect on THW is likely due to sensory and/or cognitive adaptation processes. Behavioral adaptation effects need to be taken into account in future implementations of automated convoy systems. Potential application areas of this research comprise automated freight traffic (truck convoys) and the design of driver assistance systems in general. Potential countermeasures against following at short distance as behavioral adaptation should be considered.

How to Manage Conflict and Ambiguities in Localization and Map Matching

AbstractSince the use of systems of satellite positioning such as the global positioning system (GPS), applications have tried to locate vehicles on maps representing the environment with their attributes. For one decade, this has led to both localization and navigation services for users. Recently, new researches have begun in order to extend the functionalities of the existing systems and thus to develop new applications using these technologies in the design of driver assistance systems. These new systems will indeed allow us to anticipate road departures or prevent overspeed turn approaches. Nevertheless, to deploy such new functionalities, it is imperative to ensure the association of vehicle position with one of the roadmap segments. In this article, we propose a new approach based on the belief theory taking into account the imperfections of available data in order to ensure the positioning and tracking of a vehicle on a roadmap and to manage conflicts and ambiguities using a multi-hypotheses decision.

Adjacent Lane Detection and Lateral Vehicle Distance Measurement Using Vision-Based Neuro-Fuzzy Approaches

The aim of this article attempts to propose an advanced design of driver assistance system which can provide thedriver advisable information about the adjacent lanes and approaching lateral vehicles. The experimental vehiclehas a camera mounted at the left side rear view mirror which captures the images of adjacent lane. The detectionof lane lines is implemented with methods based on image processing techniques. The candidates for lateralvehicle are explored with lane-based transformation, and each one is verified with the characteristics of its length,width, time duration, and height. Finally, the distances of lateral vehicles are estimated with the well-trainedrecurrent functional neuro-fuzzy network. The system is tested with nine video sequences captured when thevehicle is driving on Taiwan’s highway, and the experimental results show it works well for different road conditionsand for multiple vehicles.

Factors correlated with traffic accidents as a basis for evaluating Advanced Driver Assistance Systems

This study aims to identify factors which influence and cause errors in traffic accidents and to use these as a basis for information to guide the application and design of driver assistance systems. A total of 474 accidents were examined in depth for this study by means of a psychological survey, data from accident reports, and technical reconstruction information. An error analysis was subsequently carried out, taking into account the driver, environment, and vehicle sub-systems. Results showed that all accidents were influenced by errors as a consequence of distraction and reduced activity. For crossroad accidents, there were further errors resulting from sight obstruction, masked stimuli, focus errors, and law infringements. Lane departure crashes were additionally caused by errors as a result of masked stimuli, law infringements, expectation errors as well as objective and action slips, while same direction accidents occurred additionally because of focus errors, expectation errors, and objective and action slips. Most accidents were influenced by multiple factors. There is a safety potential for Advanced Driver Assistance Systems (ADAS), which support the driver in information assimilation and help to avoid distraction and reduced activity. The design of the ADAS is dependent on the specific influencing factors of the accident type.