This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Pedestrian and Vehicle Recognition Based on Radar for Autonomous Emergency Braking
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Autonomous Emergency Braking Systems (AEBS) usually contain radar, (stereo) camera and/or LiDAR-based technology to identify potential collision partners ahead of the car, such that to warn the driver or automatically brake to avoid or mitigate a crash. The advantage of camera is less cost: however, is inevitable to face the defects of cameras in AEBS, that is, the image recognition cannot perform good accuracy in the poor or over-exposure light condition. Therefore, the compensation of other sensors is of importance. Motivated by the improvement of false detection, we propose a Pedestrian-and-Vehicle Recognition (PVR) algorithm based on radar to apply to AEBS. The PVR employs the radar cross section (RCS) and standard deviation of width of obstacle to determine whether a threshold value of RCS and standard deviation of width of the pedestrian and vehicle is crossed, and to identity that the objective is a pedestrian or vehicle, respectively. The performance of the proposed algorithm is pressed via the experimental test data.
CitationWu, T., "Pedestrian and Vehicle Recognition Based on Radar for Autonomous Emergency Braking," SAE Technical Paper 2017-01-1405, 2017, https://doi.org/10.4271/2017-01-1405.
Data Sets - Support Documents
|Unnamed Dataset 1|
|Unnamed Dataset 2|
|Unnamed Dataset 3|
- Euro NCAP Euro NCAP to drive availability of Autonomous Emergency Braking systems for safer cars in Europ Jun. 2012 http://www.euroncap.com/Content-Web-Article/c79b2bdc-f914-4ad0-8d49-54254cda0ddc/euro-ncap-to-drive-availability-of-autonomous-emer.aspx
- Segata , M. and Cigno R. L. , Automatic Emergency Braking: Realistic Analysis of Car Dynamics and Network Performance IEEE Trans. Veh. Technol 62 9 4150 4161 2013
- Kumar S. , Gupta D. , and Yadav S. Sensor Fusion of Laser & Stereo Vision Camera for Depth Estimation and Obstacle Avoidance Int. J. of Comput. Appl 1 26 20 25 2010
- Wai R. J. and Lin Y. W. Adaptive Moving-target Tracking Control of A Vision-Based Mobile Robot via A Dynamic Petri Recurrent Fuzzy Neural Network IEEE Trans. Fuzzy Syst 21 4 688 701 2013
- Vidal-Calleja T. A. , Sanfeliu A. , and Andrade-Cetto J. Action Selection for Single-Camera SLAM IEEE Trans. Syst., Man, and Cy. B 40 6 1567 1581 2010
- Blackman S. and Popoli R. Design and Analysis of Modern Tracking System Artech House 1999
- Alessandretti G. , Broggi A. and Cerri. P Vehicle and Guard Rail Detection Using Radar and Vision Data Fusion IEEE Transactions on Intelligent Transportation Systems 8 1 95 105 2007
- Liu X. , Sun Z. and He H. On-road Vehicle Detection Fusing Radar and Vision Vehicular Electronics and Safety (ICVES) 150 154 201l
- Yang S. , Song B. and Urn J. Radar and Vision Sensor Fusion for Primary Vehicle Detection Jounal of Institute of Control, Robotics and System 16 7 639 645 2010
- Belgiovane D. , Chen C.-C. , Chen M. , Chien S. Y.-P , and Sherony R. 77 GHz Radar Scattering Properties of Pedestrians Radar Conference 2014 IEEE 0735 0738 2014
- Fortuny-Guasch J. and Chareau J.-M. Radar Cross Section Measurements of Pedestrian Dummies and Humans in the 24/77 GHz Frequency Bands JRC Scientific and Policy Report 2013
- LeBlanc D. J. , Gilbert M. , Stachowski S. , Blower D. , Flannagan C. A. , Karamihas S. , Buller W. T. , and Sherony R. Advanced Surrogate Target Development for Evaluating Pre-Collision Systems 23rd Enhanced Safety Vehicles Conference 2013