Development of a Multimedia-Based Vehicle Lane Departure Warning, Forward Collision Warning and Event Video Recorder Systems

2007 ◽  
Author(s):  
Che-Chung Lin ◽  
His-Wen Hsieh ◽  
Dau-Chen Huang ◽  
Chi-Wei Lin ◽  
Yung-Sheng Liao ◽  
...  
Keyword(s):  
Lane Departure Warning ◽  
Collision Warning ◽  
Lane Departure ◽  
Video Recorder ◽  
Forward Collision Warning

scholarly journals Effectiveness of ADAS in Existing Vehicles

2021 ◽  
Vol 9 (VI) ◽  
pp. 2471-2472
Author(s):  
Arpit S Agarkar
Keyword(s):  
Driver Assistance System ◽  
Raspberry Pi ◽  
Lane Departure Warning ◽  
Driver Assistance ◽  
Warning Signals ◽  
Human Errors ◽  
Collision Warning ◽  
Radar Sensors ◽  
Lane Departure ◽  
Forward Collision Warning

Around the whole world, lot of accidents occur due to inattentive driving and human errors. Partial Autonomous Driver Assistance System can be of lot of help to the driver in avoiding the collision and maintain the control of the vehicle by giving different warning signals. The system consists of (i) Forward Collision Warning (ii) Lane Departure Warning. In the existing vehicles, by adding RADAR Sensors and Camera and a microcontroller for processing, it can be used to track the lanes and gives an acoustic warning in advance if there is lane departure or any vehicle or pedestrian ahead. The proposed system can be implemented on raspberry pi microcontroller board.

scholarly journals Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

2021 ◽  
Vol 13 (16) ◽  
pp. 8759
Author(s):  
Wei Ye ◽  
Yueru Xu ◽  
Feixiang Zhou ◽  
Xiaomeng Shi ◽  
Zhirui Ye
Keyword(s):  
Mixed Model ◽  
Warning System ◽  
Gaussian Mixture ◽  
Bus Drivers ◽  
Collision Warning ◽  
Lane Departure ◽  
Driving Assistance ◽  
Naturalistic Driving ◽  
Forward Collision Warning ◽  
Driving Assistance System

Road crashes cause serious loss of life and property. Among all vehicles, buses are more likely to encounter crashes. In recent years, the advanced driving assistance system (ADAS) has been widely used in buses to improve safety. The warning system is one of the key functions and has proven effective in reducing crashes. However, drivers often ignore or overreact to ADAS warnings during naturalistic driving scenarios. Therefore, reactions of bus drivers to warnings need further investigation. In this study, bus drivers’ responses to lane departure warning (LDW) and forward collision warning (FCW) were investigated using 20-day naturalistic driving data. These reactions could be classified into three categories, namely positive, negative, and overreaction or emergency, by employing the Gaussian mixture model. The authors constructed a framework to quantify drivers’ reactions to the warning and study the reaction characteristics in different environments. The results indicate that drivers’ reactions to FCW were more positive than to LDW, drivers reacted more positively to LDW and FCW while driving on highways than on urban roads, and drivers reacted more positively at night to LDW and FCW than during daytime. This study gives support to an adaptive ADAS considering varying bus driver characteristics and environments.

scholarly journals Using ADAS to Future-Proof Roads—Comparison of Fog Line Detection from an In-Vehicle Camera and Mobile Retroreflectometer

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1737
Author(s):  
Ane Dalsnes Storsæter ◽  
Kelly Pitera ◽  
Edward McCormack
Keyword(s):  
Warning System ◽  
Management Systems ◽  
Warning Systems ◽  
Lane Departure Warning ◽  
Automated Driving ◽  
Pavement Markings ◽  
Lighting Conditions ◽  
Lane Marking ◽  
Lane Departure ◽  
Pavement Marking

Pavement markings are used to convey positioning information to both humans and automated driving systems. As automated driving is increasingly being adopted to support safety, it is important to understand how successfully sensor systems can interpret these markings. In this effort, an in-vehicle lane departure warning system was compared to data collected simultaneously from an externally mounted mobile retroreflectometer. The test, performed over 200 km of driving on three different routes in variable lighting conditions and road classes found that, depending on conditions, the retroreflectometer could predict whether the car’s lane departure systems would detect markings in 92% to 98% of cases. The test demonstrated that automated driving systems can be used to monitor the state of pavement markings and can provide input on how to design and maintain road infrastructure to support automated driving features. Since data about the condition of lane marking from multiple lane departure warning systems (crowd-sourced data) can provide input into the pavement marking management systems operated by many road owners, these findings also indicate that these automated driving sensors have an important role in enhancing the maintenance of pavement markings.

scholarly journals Lane Departure Warning Estimation Using Yaw Acceleration

2020 ◽  
Vol 11 (1) ◽  
pp. 102-111
Author(s):  
Em Poh Ping ◽  
J. Hossen ◽  
Wong Eng Kiong
Keyword(s):  
Vehicle Dynamics ◽  
Dynamic Responses ◽  
Dynamics Simulation ◽  
Steering Wheel ◽  
Mathematical Representation ◽  
Vehicle Speed ◽  
Lane Departure Warning ◽  
Model Based ◽  
Lane Departure ◽  
Proposed Model

AbstractLane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

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