scholarly journals Design and Implementation Procedure for an Advanced Driver Assistance System Based on an Open Source AUTOSAR

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1025 ◽  
Author(s):  
Park ◽  
Choi
Keyword(s):  
Open Source ◽  
System Architecture ◽  
Driver Assistance System ◽  
Warning System ◽  
Evaluation Methods ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Collision Warning ◽  
Design And Implementation

In this paper, we present the detailed design and implementation procedures for an advanced driver assistance system (ADAS) based on an open source automotive open system architecture (AUTOSAR). Due to the increasing software complexity of ADAS, portability, component interoperability, and maintenance are becoming essential development factors. AUTOSAR satisfies these demands by defining system architecture standards. Although commercial distributions of AUTOSAR are well established, accessibility is a huge concern as they come with very expensive licensing fees. Open source AUTOSAR addresses this issue and can also minimize the overall cost of development. However, the development procedure has not been well established and could be difficult for engineers. The contribution of this paper is divided into two main parts: First, we provide the complete details on developing a collision warning system using an open source AUTOSAR. This includes the simplified basic concepts of AUTOSAR, which we have organized for easier understanding. Also, we present an improvement of the existing AUTOSAR development methodology focusing on defining the underlying tools at each development stage with clarity. Second, as the performance of open source software has not been proven and requires benchmarking to ensure the stability of the system, we propose various evaluation methods measuring the real-time performance of tasks and the overall latency of the communication stack. These performance metrics are relevant to confirm whether the entire system has deterministic behavior and responsiveness. The evaluation results can help developers to improve the overall safety of the vehicular system. Experiments are conducted on an AUTOSAR evaluation kit integrated with our self-developed collision warning system. The procedures and evaluation methods are applicable not only on detecting obstacles but other variants of ADAS and are very useful in integrating open source AUTOSAR to various vehicular applications.

scholarly journals Advanced Driver Assistance System

2020 ◽  
Vol 8 (6) ◽  
pp. 3481-3487
Keyword(s):  
Automobile Industry ◽  
Human Error ◽  
Driver Assistance System ◽  
Warning System ◽  
Modern Technology ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Multiple Sources ◽  
Safe Driving

The prime motive of the automobile industry is to improve safety in driving machines and avoid accidents. Traffic rules and regulations drafted by the law aren’t followed by many citizens. This is another reason for an accident. Accidents sometimes are unintentional. Some serious acts like drunk and drive, ignoring the signboards, and over speeding might result in severe causalities. To prevent situations like these we seek the Advanced Driver Assistance System (ADAS). ADAS has the potential to increase safety and provide comfort driving. Driving situations are electronically controlled and decisions are simplified for the driver. Old people may also receive plenty of benefits from this technology. ADAS is designed with a humanmachine interface which tends to improve road safety marginally. Accidents caused by human error can also be minimized. ADAS helps the driver to automate, adapt and enhance the vehicle system for safe driving. Passive safety technologies like wearing seatbelts and airbags cannot prevent road fatalities. Modern technology like ADAS is different from traditional and passive technology and minimizes the fatalities consistently. ADAS also alert the driver of potential problems and helps in maintaining the stability of the vehicle under critical circumstances. Safety features are implemented to take control of the vehicle during collisions. ADAS relies on inputs from multiple sources like the brake assistant, pressure control system, lane departure warning system, road sign identification, etc. Additional features can also be customized based on the needs of the driver. In this paper methods to prevent over speeding, vehicle collisions, and driver alertness systems are discussed. RFID readers are used for sensing the speed limit in the signboards. The speed of the vehicle is managed based on the reading obtained from the tags. Sensors like ultrasonic, alcohol detector, gas sensor, temperature sensor are used to measure other parameters to enhance the safety measure while driving.

scholarly journals Design and Implementation of Driver Assistance System using Machine Learning

2020 ◽  
Vol 994 ◽  
pp. 012006
Author(s):  
D. S. Bhargava ◽  
N. Shyam ◽  
K. Senthil Kumar ◽  
M. Wasim Raja ◽  
P Sivashankar.
Keyword(s):  
Machine Learning ◽  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance ◽  
Design And Implementation

scholarly journals Advanced Driver-Assistance System (ADAS) for Intelligent Transportation Based on the Recognition of Traffic Cones

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Liyong Wang ◽  
Peng Sun ◽  
Min Xie ◽  
Shaobo Ma ◽  
Boxiong Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Machine Vision ◽  
Driver Assistance System ◽  
Vision System ◽  
Intelligent Transportation ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Depth Information ◽  
Driver Assistance ◽  
Temporary Road

Great changes have taken place in automation and machine vision technology in recent years. Meanwhile, the demands for driving safety, efficiency, and intelligence have also increased significantly. More and more attention has been paid to the research on advanced driver-assistance system (ADAS) as one of the most important functions in intelligent transportation. Compared with traditional transportation, ADAS is superior in ensuring passenger safety, optimizing path planning, and improving driving control, especially in an autopilot mode. However, level 3 and above of the autopilot are still unavailable due to the complexity of traffic situations, for example, detection of a temporary road created by traffic cones. In this paper, an analysis of traffic-cone detection is conducted to assist with path planning under special traffic conditions. A special machine vision system with two monochrome cameras and two color cameras was used to recognize the color and position of the traffic cones. The result indicates that this novel method could recognize the red, blue, and yellow traffic cones with 85%, 100%, and 100% success rate, respectively, while maintaining 90% accuracy in traffic-cone distance sensing. Additionally, a successful autopilot road experiment was conducted, proving that combining color and depth information for recognition of temporary road conditions is a promising development for intelligent transportation of the future.

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