Modelling of Safe Driving Assistance System for Automotive and Prediction of Accident Rates

2019 ◽  
Vol 10 (1) ◽  
pp. 61-77
Author(s):  
Debraj Bhattacharjee ◽  
Prabha Bhola ◽  
Pranab K. Dan
Keyword(s):  
Fuzzy Inference ◽  
Warning System ◽  
Assistance System ◽  
Inference System ◽  
Safe Driving ◽  
Driving Assistance ◽  
System Module ◽  
Accident Rates ◽  
The Right ◽  
Driving Assistance System

This research article attempts to analytically determine the factors, significant for safety, in connection with driving of automotives as well as to develop a conceptual model of the driving assistance system, using the knowledge about such factors. Millions of casualties due to road accidents, happen worldwide every year and the annual average of lives lost in India alone is about hundred and fifty thousand. The causes of such accidents are attributed to road characteristic and condition, driving faults, driving conditions or traffic environmental factors and defects or functional failure in vehicle mechanism. Studies have focused primarily on these factors without associating the ‘weather' which has been reported as in a work but as an isolated factor without including the above three. This work includes all the four stated factors in modelling the driver assistance system for automatic speed control with warning system module. Further, to predict accident rates in a particular region a model using adaptive neuro fuzzy inference system (ANFIS) is proposed in this work, which may be used by the vehicle manufactures to select the right product variant to minimise accidents.

Utterance Generation Based on Driving Evaluation for Driving Assistance Robot

2016 ◽  
Vol 20 (5) ◽  
pp. 836-844 ◽  
Author(s):  
Yoshikazu Okajima ◽  
◽  
Hiroyuki Masuta ◽  
Masatoshi Okumura ◽  
Tatsuo Motoyoshi ◽  
...  
Keyword(s):  
Evaluation System ◽  
Fuzzy Inference ◽  
Estimation Method ◽  
Assistance System ◽  
Human Beings ◽  
Robot Interaction ◽  
Timing Estimation ◽  
Driving Assistance ◽  
Utterance Generation ◽  
Driving Assistance System

This manuscript describes a robot interaction for the driving assistance system of an Ultra-Compact Electric Vehicle (UCEV). Fun-to-drive and safety are important for improving the commercial value of UCEV. To improve fun-to-drive and safety, the improvement of the driving skills is important. However, the driving assistance system of an ordinary vehicle only considers the objective driving evaluation. Therefore, we propose an interactive driving assistance system that considers the relation between the subjective as well as the objective driving evaluation. Furthermore, we install a communicating robot within a UCEV to interact with human beings in real time. As a first step, we propose a driving evaluation system by applying a simplified fuzzy inference, and an interaction timing estimation method by applying a spiking neural network. Through an off-line simulation experiment, we verify the effectiveness of our proposal that is able to generate a robot utterances content as well as estimate reasonable timing.

CASA: An Alternative Smartphone-Based ADAS

2021 ◽  
pp. 1-41
Author(s):  
Manolo Dulva Hina ◽  
Hongyu Guan ◽  
Assia Soukane ◽  
Amar Ramdane-Cherif
Keyword(s):  
Low Cost ◽  
Electronic System ◽  
Time Data ◽  
Case Scenario ◽  
Safe Driving ◽  
The Road ◽  
Driving Assistance ◽  
Alternative Means ◽  
On The Road ◽  
Driving Assistance System

Advanced driving assistance system (ADAS) is an electronic system that helps the driver navigate roads safely. A typical ADAS, however, is suited to specific brands of vehicle and, due to proprietary restrictions, has non-extendable features. Project CASA is an alternative, low-cost generic ADAS. It is an app deployable on smartphone or tablet. The real-time data needed by the app to make sense of its environment are stored in the vehicle or on the cloud, and are accessible as web services. They are used to determine the current driving context, and, if needed, decide actions to prevent an accident or keep road navigation safe. Project CASA is an undertaking of a consortium of industrial and academic partners. A use case scenario is tested in the laboratory (virtual) and on the road (actual) to validate the appropriateness of CASA. It is a contribution to safe driving. CASA’s contribution also lies in its approach in the semantic modeling of the context of the environment, the vehicle and the driver, and on the modeling of rules for fusion of data and fission process yielding an action to be implemented. In addition, CASA proposes a secured means of transmitting data using light, via light fidelity (LiFi), itself an alternative means of wireless vehicle–smartphone communication.

Sign in / Sign up

Export Citation Format

Share Document