scholarly journals Communication Network Architectures for Driver Assistance Systems

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6867
Author(s):  
Romeo Giuliano ◽  
Franco Mazzenga ◽  
Eros Innocenti ◽  
Francesca Fallucchi ◽  
Ibrahim Habib
Keyword(s):  
Local Area ◽  
Network Architectures ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Alert Message ◽  
Communication Time ◽  
Communication Architectures ◽  
Assistance Systems ◽  
Time Required

Autonomous Driver Assistance Systems (ADAS) are of increasing importance to warn vehicle drivers of potential dangerous situations. In this paper, we propose one system to warn drivers of the presence of pedestrians crossing the road. The considered ADAS adopts a CNN-based pedestrian detector (PD) using the images captured from a local camera and to generate alarms. Warning messages are then forwarded to vehicle drivers approaching the crossroad by means of a communication infrastructure using public radio networks and/or local area wireless technologies. Three possible communication architectures for ADAS are presented and analyzed in this paper. One format for the alert message is also presented. Performance of the PDs are analyzed in terms of accuracy, precision, and recall. Results show that the accuracy of the PD varies from 70% to 100% depending on the resolution of the videos. The effectiveness of each of the considered communication solutions for ADAS is evaluated in terms of the time required to forward the alert message to drivers. The overall latency including the PD processing and the alert communication time is then used to define the vehicle braking curve, which is required to avoid collision with the pedestrian at the crossroad.

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%.

scholarly journals The choice of rational complex of active safety systems for smart cars

2021 ◽  
Vol 2061 (1) ◽  
pp. 012128
Author(s):  
A I Markovnina ◽  
N D Tsyganov ◽  
A V Papunin ◽  
V S Makarov ◽  
V V Belyakov
Keyword(s):  
Road Safety ◽  
Vehicle Safety ◽  
Active Safety ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Active Safety Systems ◽  
Assistance Systems ◽  
Smart Cars ◽  
Safety Systems

Abstract The problem of ensuring road safety affects all elements of the Driver-Car-Road-Environment system. Smart cars equipped with enough traffic assistants can significantly improve road safety. Active vehicle safety systems, including intelligent driver assistance systems and assistants, perform similar road safety functions. With all the variety of possibilities for equipping cars with systems complexes, the need arises to assess the feasibility and profitability of installing a particular complex of systems. For this, it is proposed to apply the methods of multi-criteria assessment. As a result of calculations, the best options for the sets of systems that most widely cover the road situation have been identified.

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.

Improved Vision Based Driving Space Analysis by Condensation of Image Data

2012 ◽  
Vol 605-607 ◽  
pp. 2260-2264
Author(s):  
Yan Fen Mao ◽  
Hans Wiedmann ◽  
Ming Chen
Keyword(s):  
Image Data ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Detection And Tracking ◽  
Detection And Identification ◽  
Diversity Sampling ◽  
Assistance Systems ◽  
On The Road ◽  
Improve Reliability

Sophisticated ADAS (Advanced Driver Assistance Systems) use vision based methods for detection and keeping track of ahead driving cars. With thus acquired data it is possible to implement e.g. following up functions to automatically keep equal distance to ahead driving vehicles or avoid collisions with obstacles ahead. Known vision based methods for detection and tracking of vehicles use its underneath shadow on the road. The main drawbacks of those methods are the detection and identification of a shadow belonging to a vehicle is neither reliable nor robust, and the thereto required processing of the camera images is very expensive concerning processing time. To improve reliability and detectability we propose here to use an approach which is different from the known methods a nonparametric one; to improve processing speed we propose to apply diversity-sampling to condense the image data before processing it.

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.

Correlating driver gaze with the road scene for driver assistance systems

2005 ◽  
Vol 52 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Luke Fletcher ◽  
Gareth Loy ◽  
Nick Barnes ◽  
Alexander Zelinsky
Keyword(s):  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Assistance Systems

scholarly journals Performance Guarantees for Hazard Based Lateral Vehicle Control

2002 ◽  
Author(s):  
Eric J. Rossetter ◽  
J. Christian Gerdes
Keyword(s):  
Control Force ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driving Experience ◽  
The Road ◽  
Performance Guarantees ◽  
System A ◽  
Assistance Systems ◽  
On The Road ◽  
Near Future

Today’s vehicles are incorporating many advanced driver assistance systems and in the near future it will be likely to have increased capabilities such as lanekeeping assist systems. These systems will be an integral part of the driving experience, aiding the driver in avoiding hazardous obstacles. One approach for these systems is to represent the hazards as artificial potential fields that add control inputs to move the vehicle towards safe regions on the road. This paper focuses on bounding the lateral motion of a vehicle for a lanekeeping system. A Lyapunov approach is used where the bounding function consists of the artificial potential energy associated with the controller, the kinetic energy in the lateral and yaw modes, and energy terms that are dependent on vehicle heading. In order to achieve this bound, a condition has to be met for the lookahead distance and the location of the control force (which can also be interpreted as a condition on the decoupling of lateral and yaw modes). Using this bound, a potential field gain can be chosen to guarantee collision avoidance with fixed lateral obstacles.

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