scholarly journals Study on the Driver/Steering Wheel Interaction in Emergency Situations

2020 ◽  
Vol 10 (20) ◽  
pp. 7055
Author(s):  
Francesco Comolli ◽  
Massimiliano Gobbi ◽  
Gianpiero Mastinu
Keyword(s):  
Muscular Activity ◽  
Steering Wheel ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Emergency Situations ◽  
Relevant Effect ◽  
Hands On ◽  
Lateral Disturbance ◽  
The Moment ◽  
Three Components

Advanced driver assistance systems (ADAS) are becoming increasingly prevalent. The tuning of these systems would benefit from a deep knowledge of human behaviour, especially during emergency manoeuvres; however, this does not appear to commonly be the case. We introduced an instrumented steering wheel (ISW) to measure three components of force and three components of the moment applied by each hand, separately. Using the ISW, we studied the kick plate manoeuvre. The kick plate manoeuvre is an emergency manoeuvre to recover a lateral disturbance inducing a spin. The drivers performed the manoeuvre either keeping two hands on the steering wheel or one hand only. In both cases, a few instants after the lateral disturbance induced by the kick plate occurred, a torque peak was applied at the ISW. Such a torque appeared to be unintentional. The voluntary torque on the ISW occurred after the unintentional torque. The emergency manoeuvre performed with only one hand was quicker, since, if two hands were used, an initial fighting of the two hands against each other was present. Therefore, we propose to model the neuro-muscular activity in driver models to consider the involuntary muscular phenomena, which has a relevant effect on the vehicle dynamic response.

Instrumented Steering Wheel: Accurate Experimental Characterisation of the Forces Exerted by the Driver Hands for Future Advanced Driver Assistance Systems

2018 ◽  
Author(s):  
Francesco Comolli ◽  
Federico M. Ballo ◽  
Massimiliano Gobbi ◽  
Gianpiero Mastinu
Keyword(s):  
Steering Wheel ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Plate Test ◽  
Driving Behaviors ◽  
Load Cells ◽  
Assistance Systems ◽  
Lateral Disturbance ◽  
Experimental Characterisation

The interaction between driver and vehicle is analyzed in the paper. The driver acts on the steering wheel to modify the trajectory and to control the vehicle during panic situations. The knowledge of the forces exerted by the driver at the steering wheel is useful for a better understanding of the driver steering action. The final aim is to inspire the development of haptic steering wheels for better tuning of Advanced Driver Assistance Systems (ADAS). An instrumented steering wheel has been used, which includes two six axis load cells to measure the forces and the moments exerted by the driver hands and six sensors used to measure the grip strength. Two maneuvers have been considered, a moderate speed turn and a kick plate test which simulates a panic situation with an impulsive lateral disturbance. For both of the two considered situations, some common driving behaviors have been highlighted and analyzed. The preliminary results encourage the development of haptic instrumented steering wheels, able to improve ADAS. Actually it seems possible to infer the driver steering purpose before the steering wheel is actually rotated.

DETECTION OF TRAFFIC SIGNALS USING COLOR SEGMENTATION AND A RADIAL SYMMETRY DETECTOR

2021 ◽  
pp. 25-33
Author(s):  
Р.М. Шакирзянов
Keyword(s):  
Traffic Signals ◽  
Radial Symmetry ◽  
Unmanned Vehicles ◽  
Color Segmentation ◽  
Morphological Operations ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Traffic Lights ◽  
Emergency Situations ◽  
Road Signs

В настоящее время широкое распространение получают беспилотные системы управления различными транспортными средствами, в том числе автомобилями. Управление беспилотным автомобилем предполагает решение задач, связанных с распознаванием объектов дорожной обстановки: пешеходов, автомобилей, препятствий (в виде ям, кочек, столбов, деревьев, зданий и т.д.), дорожных знаков, разметки, светофоров. Предложен алгоритм решения задачи обнаружения и распознавания сигналов светофоров круглой формы. Для решения этой задачи задействованы: быстрое преобразование радиальной симметрии, цветовая сегментация, морфологические операции. Особенностью алгоритма является то, что области расположения световых сигналов предварительно определяются по цветовому признаку с последующим уточнением формы и положения объектов на изображении. На основе предложенного метода было разработано программное обеспечение для обнаружения сигналов светофоров на фотоснимках. Программное обеспечение было протестировано на общедоступной базе изображений, содержащей светофоры. Предлагаемый алгоритм показал работоспособность, он может быть расширен в части типов распознаваемых сигналов и применён в составе систем управления беспилотными транспортными средствами, а также в составе систем помощи водителю для решения задач по предупреждению опасных и аварийных ситуаций на транспорте Currently, unmanned systems for controlling various vehicles, including cars, are becoming widespread. Driving an unmanned vehicle involves solving problems related to the recognition of traffic objects: pedestrians, cars, obstacles (in the form of holes, bumps, poles, trees, buildings, etc.), road signs, markings, traffic lights. An algorithm for solving the problem of detecting and recognizing circular traffic signals is proposed. To solve this problem, the following are involved: rapid transformation of radial symmetry, color segmentation, morphological operations. A feature of the algorithm is that the areas of the location of the light signals are preliminarily determined by color, followed by the refinement of the shape and position of objects in the image. Based on the proposed method, software was developed for detecting traffic signals in photographs. The software was tested on a publicly available database of images containing traffic lights. The proposed algorithm has shown its efficiency, it can be expanded in terms of the types of signals recognized and used as part of control systems for unmanned vehicles, as well as part of driver assistance systems for solving problems to prevent dangerous and emergency situations

scholarly journals Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand

2019 ◽  
Vol 11 (3) ◽  
pp. 59-70
Author(s):  
Dina Kanaan ◽  
Suzan Ayas ◽  
Birsen Donmez ◽  
Martina Risteska ◽  
Joyita Chakraborty
Keyword(s):  
Motor Control ◽  
Secondary Task ◽  
Markov Models ◽  
Task Engagement ◽  
Steering Wheel ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Environmental Demand ◽  
Driving Task ◽  
Behavior Models

This research utilized vehicle-based measures from a naturalistic driving dataset to detect distraction as indicated by long off-path glances (≥ 2 s) and whether the driver was engaged in a secondary (non-driving) task or not, as well as to estimate motor control difficulty associated with the driving environment (i.e. curvature and poor surface conditions). Advanced driver assistance systems can exploit such driver behavior models to better support the driver and improve safety. Given the temporal nature of vehicle-based measures, Hidden Markov Models (HMMs) were utilized; GPS speed and steering wheel position were used to classify the existence of off-path glances (yes vs. no) and secondary task engagement (yes vs. no); lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher). Best classification accuracies were achieved for identifying cases of long off-path glances and secondary task engagement with both accuracies of 77%.

scholarly journals Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Wenshuo Wang ◽  
Junqiang Xi ◽  
Huiyan Chen
Keyword(s):  
Driver Behavior ◽  
Control Point ◽  
Point Of View ◽  
Driver Model ◽  
Transport Systems ◽  
Steering Wheel ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Wide Range ◽  
Vehicle Systems

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described.

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

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 233 ◽  
Author(s):  
Nadja Schömig ◽  
Katharina Wiedemann ◽  
Sebastian Hergeth ◽  
Yannick Forster ◽  
Jeffrey Muttart ◽  
...  
Keyword(s):  
Evaluation Methods ◽  
Assessment Method ◽  
Evaluation Methodology ◽  
Santa Fe ◽  
Driver Assistance ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Hands On ◽  
Assistance Systems

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.

Third-Party Vehicle Data Collection for Advanced Driver Assistance Systems Analysis

2021 ◽  
pp. 036119812110391
Author(s):  
Kathleen Berman ◽  
Keith Campbell ◽  
Valerie Gawron ◽  
Jeffrey Long ◽  
Samir Yuha
Keyword(s):  
Can Bus ◽  
Third Party ◽  
Steering Wheel ◽  
Gps Data ◽  
Area Network ◽  
Cruise Control ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Logs of vehicle controller area network (CAN) bus traffic supplemented by accelerometer and GPS data can provide valuable information about the use and operation of advanced driver assistance systems (ADAS) to the broader safety research community. Although CAN bus message codes are often manufacturer-specific, third-party libraries provide partial decoding of messages from many vehicle models, which can be augmented by reverse-engineering additional signals. This study explored the value of CAN bus, accelerometer, and GPS data that were logged on a variety of light vehicle models with an emphasis on availability of lane keep assist/lane departure warning, and adaptive cruise control. This study demonstrated that in-vehicle ADAS variables such as system on/off status and whether the system is actively controlling the vehicle could be determined on a variety of vehicle types. Associated control variables such as steering wheel angle, gas pedal state, and brake pedal state could also be determined from most vehicles tested. CAN messages, together with roadway features identified via GPS location, can provide a richer understanding of ADAS efficacy. Comparisons of message structure between models may also inform standardization efforts for electronic data recorders and telematics.

scholarly journals Experimental Validation on Intersection Turning Trajectory Prediction Method for Advanced Driver Assistance System Based on Triclothoidal Curve

2021 ◽  
Vol 11 (13) ◽  
pp. 5900
Author(s):  
Yohei Fujinami ◽  
Pongsathorn Raksincharoensak ◽  
Shunsaku Arita ◽  
Rei Kato
Keyword(s):  
Traffic Accidents ◽  
Computational Cost ◽  
Prediction Method ◽  
Smooth Curve ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Left Turn ◽  
Left Hand

Advanced driver assistance systems (ADAS) for crash avoidance, when making a right-turn in left-hand traffic or left-turn in right-hand traffic, are expected to further reduce the number of traffic accidents caused by automobiles. Accurate future trajectory prediction of an ego vehicle for risk prediction is important to activate the assistance system correctly. Our objectives are to propose a trajectory prediction method for ADAS for safe intersection turnings and to evaluate the effectiveness of the proposed prediction method. Our proposed curve generation method is capable of generating a smooth curve without discontinuities in the curvature. By incorporating the curve generation method into the vehicle trajectory prediction, the proposed method could simulate the actual driving path of human drivers at a low computational cost. The curve would be required to define positions, angles, and curvatures at its initial and terminal points. Driving experiments conducted at real city traffic intersections proved that the proposed method could predict the trajectory with a high degree of accuracy for various shapes and sizes of the intersections. This paper also describes a method to determine the terminal conditions of the curve generation method from intersection features. We set a hypothesis where the conditions can be defined individually from intersection geometry. From the hypothesis, a formula to determine the parameter was derived empirically from the driving experiments. Public road driving experiments indicated that the parameters for the trajectory prediction could be appropriately estimated by the obtained empirical formula.

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