Visualization of Headlight Illumination for the Virtual Prototyping of Light-Based Driver Assistance Systems

2015 ◽  
Author(s):  
Jan Berssenbrügge ◽  
Ansgar Trächtler ◽  
Christoph Schmidt
Keyword(s):  
Driving Simulator ◽  
Virtual Prototyping ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Simulators ◽  
Visualization System ◽  
The Road ◽  
Assistance Systems ◽  
Lighting Systems

Driving simulators that are capable of a simulation of a virtual drive at night are increasingly used for the virtual prototyping of light-based driver assistance systems. Here, the interplay between driver and assistance system, which enhances the illumination of the road ahead of the vehicle, is investigated. For such investigations, special driving simulators are applied that enable not only a standard driving simulation but also cover the special requirements for the visualization of a driving scenery at night, the simulation of automotive headlights during a virtual drive at night, and the interface to a headlight control module (HCM) that operates the physical headlight prototypes. In this paper, we present the visualization system of the reconfigurable driving simulator from the research project TRAFFIS. We describe the special application focus on the virtual prototyping of a light-based driver assistance system from our project partner Varroc Lighting Systems. The light-based DAS bases on a headlight prototype that combines a glare-free high beam (GFHB) function and a predictive adaptive frontlighting system (PAFS) for glare-free driving with maximized headlight time.

Visualization of Headlight Illumination for the Virtual Prototyping of Light-Based Driver–Assistance Systems

2016 ◽  
Vol 16 (3) ◽  
Author(s):  
Jan Berssenbrügge ◽  
Ansgar Trächtler ◽  
Christoph Schmidt
Keyword(s):  
Driving Simulator ◽  
Virtual Prototyping ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Simulators ◽  
Visualization System ◽  
The Road ◽  
Assistance Systems ◽  
Lighting Systems ◽  
Project Partner

Driving simulators that are capable of simulating a virtual drive at night are increasingly used for the virtual prototyping of light-based driver–assistance systems (DAS). Here, the interplay between driver and assistance system, which enhances the illumination of the road ahead of the vehicle, is investigated. For such investigations, special driving simulators are applied that not only enable a standard driving simulation but also cover the special requirements for the visualization of a driving scenery at night, the simulation of automotive headlights during a virtual drive at night, and the interface to a headlight control module (HCM) that operates the physical headlight prototypes. In this paper, we present the visualization system of the reconfigurable driving simulator from the research project TRAFFIS. We describe the special application focus on the virtual prototyping of a light-based DAS from our project partner Varroc Lighting Systems. The light-based DAS is based on a headlight prototype that combines a glare-free high-beam (GFHB) function and a predictive adaptive frontlighting system (PAFS) for glare-free driving with maximized headlight time.

scholarly journals A Station with Multiple Head-Mounted Displays for Learning Advanced Driver Assistance Systems

2017 ◽  
Author(s):  
Kareem Abdelgawad ◽  
Jürgen Gausemeier ◽  
Jörg Stöcklein ◽  
Michael Grafe ◽  
Jan Berssenbrügge ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Simulators ◽  
Essential Information ◽  
Advanced Driver Assistance Systems ◽  
Head Mounted Display ◽  
Head Mounted Displays ◽  
Assistance Systems ◽  
Training Station

Advanced driver assistance systems (ADAS) are technologies that provide drivers with essential information or take over difficult and repetitive tasks. They contribute to improving road safety and increasing driving comfort. Apart from the technical development challenges, training and demonstration of ADAS in safe environments are important concerns for automobile manufacturers and suppliers. This paper presents the concept and prototypical implementation of an innovative training station for learning ADAS with driving simulators. The training station has a scalable and modular architecture, so that more than one driving simulator can be connected to a common instructor unit. Fully immersive visualization is provided by utilizing head-mounted displays for the participating driving simulators. The instructor unit consists of a computer with a developed software tool for session control, monitoring, and evaluation. Moreover, the instructor can use a head-mounted display and participate within the same virtual environment of a selected trainee. A simulation model for an autonomous driving system was implemented and a group of test persons were involved to show the usability and validity of the developed training station for ADAS learning and demonstration.

Advanced vehicle technology: Mapping mental model accuracy and system exposure to driver behavior

2021 ◽  
Vol 65 (1) ◽  
pp. 1072-1076
Author(s):  
Aaron Benson ◽  
Joanne But ◽  
John Gaspar ◽  
Cher Carney ◽  
William J. Horrey
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
Driving Simulator ◽  
Cruise Control ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driver Education ◽  
The Road ◽  
Vehicle Technology ◽  
Assistance Systems

Advanced driver assistance systems have potential to increase safety and comfort for drivers; however, drivers need to understand the capabilities and limitations of these systems to use them appropriately. This study sought to explore how the quality (accuracy) of drivers’ mental models of adaptive cruise control (ACC) impacted their behavior and interactions while using the system. Seventy-eight participants drove in a high-fidelity driving simulator while operating an ACC system, in normal conditions and while interacting with the system interface. Participants with stronger (more accurate) mental models glanced to the road ahead more often during normal conditions early on compared to drivers were less accurate mental models; however, these differences diminished with increased system exposure. Glance behavior while interacting with the system and time to complete the interactions were less effected by the strength of the participant’s mental model. Results are discussed in the context of driver education and training.

scholarly journals A Station with Multiple Head-Mounted Displays for Learning Advanced Driver Assistance Systems

2017 ◽  
Author(s):  
Kareem Abdelgawad ◽  
Jürgen Gausemeier ◽  
Jan Berssenbrügge ◽  
Jörg Stöcklein
Keyword(s):  
Driving Simulator ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Simulators ◽  
Essential Information ◽  
Advanced Driver Assistance Systems ◽  
Head Mounted Display ◽  
Head Mounted Displays ◽  
Assistance Systems ◽  
Training Station

Advanced driver assistance systems (ADAS) are technologies that provide drivers with essential information or take over difficult and repetitive tasks. They contribute to improving road safety and increasing driving comfort. Apart from the technical development challenges, training and demonstration of ADAS in safe environments are important concerns for automobile manufacturers and suppliers. This paper presents the concept and prototypical implementation of an innovative training station for learning ADAS with driving simulators. The training station has a scalable and modular architecture, so that more than one driving simulator can be connected to a common instructor unit. Fully immersive visualization is provided by utilizing head-mounted displays for the participating driving simulators. The instructor unit consists of a computer with a developed software tool for session control, monitoring, and evaluation. Moreover, the instructor can use a head-mounted display and participate within the same virtual environment of a selected trainee. A simulation model for an autonomous driving system was implemented and a group of test persons were involved to show the usability and validity of the developed training station for ADAS learning and demonstration.

Road Parameter-Based Driver Assistance System for Safe Driving

2019 ◽  
Vol 2 (4) ◽  
pp. 253-262
Author(s):  
Sai Charan Addanki ◽  
Keyword(s):  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Safe Driving ◽  
The Road ◽  
Lane Tracking ◽  
Road Type ◽  
Key Aspects ◽  
Type Classification

One of the key aspects of Advanced Driver Assistance Systems (ADAS) is ensuring the safety of the driver by maintaining a safe drivable speed. Overspeeding is one of the critical factors for accidents and vehicle rollovers, especially at road turns. This article aims to propose a driver assistance system for safe driving on Indian roads. In this regard, a camera-based classification of the road type combined with the road curvature estimation helps the driver to maintain a safe drivable speed primarily at road curves. Three Deep Convolutional Neural Network (CNN) models viz. Inception-v3, ResNet-50, and VGG-16 are being used for the task of road type classification. In this regard, the models are validated using a self-created dataset of Indian roads and an optimal performance of 83.2% correct classification is observed. For the calculation of road curvature, a lane tracking algorithm is used to estimate the curve radius of a structured road. The road type classification and the estimated road curvature values are given as inputs to a simulation-based model, CARSIM (vehicle road simulator to estimate the drivable speed). The recommended speed is then compared and analyzed with the actual speeds obtained from subjective tests.

scholarly journals Design Method of ADAS for Urban Electric Vehicle Based on Virtual Prototyping

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Katarzyna Jezierska-Krupa ◽  
Wojciech Skarka
Keyword(s):  
Design Method ◽  
Virtual Prototyping ◽  
Real Life ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Smart Power ◽  
The Road ◽  
Unique Method ◽  
On The Road ◽  
Positive Effect

Since 2012, the Smart Power Team has been actively participating in the Shell Eco-marathon, which is a worldwide competition. From the very beginning, the team has been working to increase driver’s safety on the road by developing Advanced Driver Assistance Systems. This paper presents unique method for designing ADAS systems in order to minimize the costs of the design phase and system implementation and, at the same time, to maximize the positive effect the system has on driver and vehicle safety. The described method is based on using virtual prototyping tool to simulate the system performance in real-life situations. This approach enabled an iterative design process, which resulted in reduction of errors with almost no prototyping and testing costs.

scholarly journals Detection and Tracking of Road Barrier Based on Radar and Vision Sensor Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Taeryun Kim ◽  
Bongsob Song
Keyword(s):  
Lateral Position ◽  
Vision Sensor ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Estimation Algorithms ◽  
Detection And Tracking ◽  
First Case ◽  
Lateral Distance ◽  
Assistance Systems

The detection and tracking algorithms of road barrier including tunnel and guardrail are proposed to enhance performance and reliability for driver assistance systems. Although the road barrier is one of the key features to determine a safe drivable area, it may be recognized incorrectly due to performance degradation of commercial sensors such as radar and monocular camera. Two frequent cases among many challenging problems are considered with the commercial sensors. The first case is that few tracks of radar to road barrier are detected due to material type of road barrier. The second one is inaccuracy of relative lateral position by radar, thus resulting in large variance of distance between a vehicle and road barrier. To overcome the problems, the detection and estimation algorithms of tracks corresponding to road barrier are proposed. Then, the tracking algorithm based on a probabilistic data association filter (PDAF) is used to reduce variation of lateral distance between vehicle and road barrier. Finally, the proposed algorithms are validated via field test data and their performance is compared with that of road barrier measured by lidar.

scholarly journals Implementing a Gaze Tracking Algorithm for Improving Advanced Driver Assistance Systems

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1480
Author(s):  
Agapito Ledezma ◽  
Víctor Zamora ◽  
Óscar Sipele ◽  
M. Paz Sesmero ◽  
Araceli Sanchis
Keyword(s):  
Traffic Accidents ◽  
Tracking System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Gaze Tracking ◽  
Advanced Driver Assistance Systems ◽  
The Road ◽  
Assistance Systems ◽  
On The Road ◽  
Driver Distractions

Car accidents are one of the top ten causes of death and are produced mainly by driver distractions. ADAS (Advanced Driver Assistance Systems) can warn the driver of dangerous scenarios, improving road safety, and reducing the number of traffic accidents. However, having a system that is continuously sounding alarms can be overwhelming or confusing or both, and can be counterproductive. Using the driver’s attention to build an efficient ADAS is the main contribution of this work. To obtain this “attention value” the use of a Gaze tracking is proposed. Driver’s gaze direction is a crucial factor in understanding fatal distractions, as well as discerning when it is necessary to warn the driver about risks on the road. In this paper, a real-time gaze tracking system is proposed as part of the development of an ADAS that obtains and communicates the driver’s gaze information. The developed ADAS uses gaze information to determine if the drivers are looking to the road with their full attention. This work gives a step ahead in the ADAS based on the driver, building an ADAS that warns the driver only in case of distraction. The gaze tracking system was implemented as a model-based system using a Kinect v2.0 sensor and was adjusted on a set-up environment and tested on a suitable-features driving simulation environment. The average obtained results are promising, having hit ratios between 96.37% and 81.84%.

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