scholarly journals Empirical Analysis of Safe Distance Calculation by the Stereoscopic Capturing and Processing of Images Through the Tailigator System

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5044
Author(s):  
Gerd Christian Krizek ◽  
Rene Hausleitner ◽  
Laura Böhme ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Low Cost ◽  
Field Tests ◽  
Warning System ◽  
Collision Warning ◽  
The Road ◽  
Cost System ◽  
Straight Line ◽  
Real Value ◽  
On The Road ◽  
Forward Collision Warning

Driver disregard for the minimum safety distance increases the probability of rear-end collisions. In order to contribute to active safety on the road, we propose in this work a low-cost Forward Collision Warning system that captures and processes images. Using cameras located in the rear section of a leading vehicle, this system serves the purpose of discouraging tailgating behavior from the vehicle driving behind. We perform in this paper the pertinent field tests to assess system performance, focusing on the calculated distance from the processing of images and the error margins in a straight line, as well as in a curve. Based on the evaluation results, the current version of the Tailigator can be used at speeds up to 50 km per hour without any restrictions. The measurements showed similar characteristics both on the straight line and in the curve. At close distances, between 3 and 5 m, the values deviated from the real value. At average distances, around 10 to 15 m, the Tailigator achieved the best results. From distances higher than 20 m, the deviations increased steadily with the distance. We contribute to the state of the art with an innovative low-cost system to identify tailgating behavior and raise awareness, which works independently of the rear vehicle’s communication capabilities or equipment.

scholarly journals A Forward-Collision Warning System for Electric Vehicles: Experimental Validation in Virtual and Real Environment

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4872
Author(s):  
Nicola Albarella ◽  
Francesco Masuccio ◽  
Luigi Novella ◽  
Manuela Tufo ◽  
Giovanni Fiengo
Keyword(s):  
Electric Vehicles ◽  
Low Cost ◽  
Warning System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Driver Behaviour ◽  
Collision Warning ◽  
Fast Development ◽  
Assistance Systems ◽  
Forward Collision Warning

Driver behaviour and distraction have been identified as the main causes of rear end collisions. However a promptly issued warning can reduce the severity of crashes, if not prevent them completely. This paper proposes a Forward Collision Warning System (FCW) based on information coming from a low cost forward monocular camera for low end electric vehicles. The system resorts to a Convolutional Neural Network (CNN) and does not require the reconstruction of a complete 3D model of the surrounding environment. Moreover a closed-loop simulation platform is proposed, which enables the fast development and testing of the FCW and other Advanced Driver Assistance Systems (ADAS). The system is then deployed on embedded hardware and experimentally validated on a test track.

scholarly journals Performance Improvement of Collision Warning System on Curved Road Based on Intervehicle Communication

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hong Cho ◽  
Byeong-woo Kim
Keyword(s):  
Warning System ◽  
Relative Distance ◽  
Small Radius ◽  
Radius Of Curvature ◽  
Collision Warning ◽  
The Road ◽  
Preceding Vehicle ◽  
Curved Road ◽  
Forward Collision Warning ◽  
Intervehicle Communication

The vehicle on-board sensor based Advanced Driver Assistant System possesses limitations on a small road with a small radius of curvature because of the sensor’s inability to operate in nondetectable domains. This study suggests an Improved Cooperative Collision Warning System (ICCWS) that considers the curvature of the road and is based on intervehicle communication. To predict the radius of curvature of the road, the Arc Relative Distance (ARD), the real relative distance to a preceding vehicle on a curved road has been used. The risk of collision with the preceding vehicle is decided by calculating an index of the risk of collision on a curved road using the computed ARD. The effects of ICCWS, proposed through this simulation, have been reviewed, and the improvement in performance in following a preceding vehicle has been analyzed quantitatively via comparative analysis with the conventional forward collision warning system. Accordingly, if the estimating algorithm for curvature developed in this study is applied to a real system, the performance of following a preceding vehicle can be improved without any specific changes to the system.

scholarly journals A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 928
Author(s):  
Man Kiat Wong ◽  
Tee Connie ◽  
Michael Kah Ong Goh ◽  
Li Pei Wong ◽  
Pin Shen Teh ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Safe Driving ◽  
Collision Warning ◽  
Smart Transportation ◽  
The Road ◽  
Safety Mechanism ◽  
Visual Approach ◽  
On The Road ◽  
Forward Collision Warning

Background: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. Methods: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. Results: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. Conclusions: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions.

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.

scholarly journals Key Parameters Estimation and Adaptive Warning Strategy for Rear-End Collision of Vehicle

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Xiang Song ◽  
Xu Li ◽  
Weigong Zhang
Keyword(s):  
Field Tests ◽  
Warning System ◽  
Time Estimation ◽  
Adaptive Strategy ◽  
Parameters Estimation ◽  
Collision Warning ◽  
Variable Threshold ◽  
Luenberger Observer ◽  
Safety Distance ◽  
Driving Conditions

The rear-end collision warning system requires reliable warning decision mechanism to adapt the actual driving situation. To overcome the shortcomings of existing warning methods, an adaptive strategy is proposed to address the practical aspects of the collision warning problem. The proposed strategy is based on the parameter-adaptive and variable-threshold approaches. First, several key parameter estimation algorithms are developed to provide more accurate and reliable information for subsequent warning method. They include a two-stage algorithm which contains a Kalman filter and a Luenberger observer for relative acceleration estimation, a Bayesian theory-based algorithm of estimating the road friction coefficient, and an artificial neural network for estimating the driver’s reaction time. Further, the variable-threshold warning method is designed to achieve the global warning decision. In the method, the safety distance is employed to judge the dangerous state. The calculation method of the safety distance in this paper can be adaptively adjusted according to the different driving conditions of the leading vehicle. Due to the real-time estimation of the key parameters and the adaptive calculation of the warning threshold, the strategy can adapt to various road and driving conditions. Finally, the proposed strategy is evaluated through simulation and field tests. The experimental results validate the feasibility and effectiveness of the proposed strategy.

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