scholarly journals User Preferences in the Design of Advanced Driver Assistance Systems

2021 ◽  
Vol 13 (7) ◽  
pp. 3932
Author(s):  
Sara Paiva ◽  
Xabiel García Pañeda ◽  
Victor Corcoba ◽  
Roberto García ◽  
Próspero Morán ◽  
...  
Keyword(s):  
Driver Assistance System ◽  
User Preferences ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Special Importance ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
On The Road ◽  
And Behavior

The transport network and mobility aspects are constantly changing, and major changes are expected in the coming years in terms of safety and sustainability purposes. In this paper, we present the main conclusions and analysis of data collected from a survey of drivers in Spain and Portugal regarding user preferences, highlighting the main functionalities and behavior that an advanced driver assistance system must have in order to grant it special importance on the road to prevent accidents and also to enable drivers to have a pleasant journey. Based on the results obtained from the survey, we developed and present a working prototype for an advanced driver assistance system (ADAS), its architecture and rules systems that allowed us to create and test some scenarios in a real environment.

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 A Fuzzy Rules-Based Driver Assistance System

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Jong-Chih Chien ◽  
Jiann-Der Lee ◽  
Li-Chang Liu
Keyword(s):  
Driver Assistance System ◽  
Fuzzy Rules ◽  
Assistance System ◽  
Viewing Angle ◽  
Driver Assistance ◽  
The Road ◽  
Road Condition ◽  
Driving Condition ◽  
On The Road ◽  
Blind Spots

A vision-based driver assistance system using fuzzy rules to determine whether warnings are necessary is presented. This system is comprised of four cameras, one of which focuses on the driver for estimating the driver’s viewing angle, another focuses on the road ahead for the detection of road condition ahead, and the last two cameras are on both sides of the vehicle, facing backward, for the purpose of determining whether neighboring lanes are occupied by vehicles hidden in the blind-spots. The system uses fuzzy-rules for the analysis of interactions between the driver’s gaze, whether there are vehicles ahead, and in the neighboring lanes to determine whether the current driving condition should be of concern to the driver and issues one of three levels of warnings, from safe to dangerous.

scholarly journals Tracking and climbing behavior recognition of heavy-duty trucks on roadways

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090362
Author(s):  
Lei Tang ◽  
Jingchi Jia ◽  
Zongtao Duan ◽  
Jingyu Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Driver Assistance System ◽  
Random Noise ◽  
Recognition Algorithm ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Heavy Duty ◽  
Driver Assistance ◽  
Behavior Recognition ◽  
And Behavior ◽  
Geographical Locations

The tracking and behavior recognition of heavy-duty trucks on roadways are keys for the development of automated heavy-duty trucks and an advanced driver assistance system. The spatiotemporal information of trucks from trajectory tracking and motions learnt from behavior analysis can be employed to predict possible driving risks and generate safe motion to avoid roadway accidents. This article presents a unified tracking and behavior recognition algorithm that can model the mobility of heavy-duty trucks on long inclined roadways. Random noise within the sampled elevation data is addressed by time-based segmentation to extract time-continuous samples at geographical locations. A Kalman filter is first used to distinguish error offsets from random noise and to estimate the distribution of truck elevations for different time intervals. A Markov chain Monte Carlo model is then applied to classify truck behaviors based on the change in elevation between two geographical locations. A heavy-duty truck mobility (HVMove) model is constructed based on the map information to apply the roadway geometry to the tracking and behavior recognition algorithm. We develop an extended Metropolis–Hastings algorithm to tune the parameters of the HVMove model. The proposed model is verified and evaluated through extensive experiments based on a real-world trajectory dataset covering sections of an expressway and national and provincial highways. From the experimental results, we conclude that the HVMove model provides sufficient accuracy and efficiency for automated heavy-duty trucks and advanced driver assistance system applications. In addition, HVMove can generate maps with the elevation information marked automatically.

scholarly journals A Portable Driver Assistance System Headset Using Augmented Reality

2018 ◽  
Vol 7 (3.6) ◽  
pp. 294
Author(s):  
Shantanu Misra ◽  
Vedika Parvez ◽  
Tarush Singh ◽  
E Chitra
Keyword(s):  
Augmented Reality ◽  
Driver Assistance System ◽  
Pedestrian Detection ◽  
Cost Effective ◽  
Weather Conditions ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Vehicle Collision ◽  
Life Threatening

Vehicle collision leading to life threatening accidents is a common problem which is incrementing noticeably. This necessitated the need for Driver Assistance Systems (DAS) which helps drivers sense nearby obstacles and drive safely. However, it’s inefficiency in unfavorable weather conditions, overcrowded roads, and low signal penetration rates in India posed many challenges during it’s implementation. In this paper, we present a portable Driver Assistance System that uses augmented reality for it’s working. The headset model comprises of five systems working in conjugation in order to assist the driver. The pedestrian detection module, along with the driver alert system serves to assist the driver in focusing his attention to obstacles in his line of sight. Whereas, the speech recognition, gesture recognition and GPS navigation modules together prevent the driver from getting distracted while driving. In the process of serving these two root causes of accidents, a cost effective, portable and holistic driver assistance system has been developed.  

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