This content is not included in your SAE MOBILUS subscription, or you are not logged in.
System Engineering of an Advanced Driver Assistance System
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Current Advanced Driver Assistance Systems (ADAS) often interact with the driver; aiding with either warnings or direct intervention. This work explores the development of an ADAS system to provide lane departure warning, forward collision warning, and a recommended following distance for a custom plug-in hybrid-electric vehicle. The system utilizes off-the-shelf hardware with in-house computer vision and sensor fusion algorithms to create a low-cost SAE Level 0 driver assistance system. The system utilizes a radar sensor as well as a camera to detect, classify, and track target vehicles. This work will illustrate the systems engineering methods used for outlining customer requirements, technical requirements, component selection, software development, simulation, vehicle fitment, and validation. Similar system engineering processes could be implemented for higher level SAE systems.
|Technical Paper||Robust Validation Platform of Autonomous Capability for Commercial Vehicles|
|Ground Vehicle Standard||Collision Detection Serial Data Communications Multiplex Bus|
CitationStoddart, E., Chebolu, S., and Midlam-Mohler, S., "System Engineering of an Advanced Driver Assistance System," SAE Technical Paper 2019-01-0876, 2019, https://doi.org/10.4271/2019-01-0876.
Data Sets - Support Documents
|Unnamed Dataset 1|
|Unnamed Dataset 2|
- Weigel , H , Lindner , P. , and Wanielik , G. 2009 IEEE Intelligent Vehicles Symposium Xi'an 2009 513 520
- Goldbeck , J , and Huertgen , B. Lane Detection and Tracking by Video Sensors IEEE/IEEJ/JSAI. Intelligent Transportation Systems 1999 74 79
- Giancarlo , A. and Broggi , A. Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion IEEE Transactions on Intelligent Transportation Systems 8 1 2007
- Committee, SAE International On-Road Automated Driving ,” 2016
- Campbell , J.L. , Brown , J.L. , Graving , J.S. , Richard , C.M. et al. Human Factors Design Guidance for Driver-Vehicle Interfaces (Report No. DOT HS 812 360) Washington, DC National Highway Traffic Safety Administration 2016
- Jia , Y. , Shelhamer , E. , Donahue , J. , Karayev , S. , Long , J. , Girshick , R. , Guadarrama , S. , and Darrell , T. 2014
- bpinaya n.d. DetectNetCars https://github.com/bpinaya/DetectNetCars
- Godil , A. , Bostelman , R. , Shackleford , W. , Hong , T. , and Shneier , M.
- Lee , J. and Park , B. Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm under the Connected Vehicles Environment IEEE Transactions on Intelligent Transportation Systems 13 1 88 90 2012