scholarly journals Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

Sensors ◽  
2015 ◽  
Vol 15 (12) ◽  
pp. 32188-32212 ◽  
Author(s):  
Guohua Wang ◽  
Qiong Liu
Keyword(s):  
Pedestrian Detection ◽  
Far Infrared ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Gradient Based ◽  
Assistance Systems

scholarly journals From Antenna Design to High Fidelity, Full Physics Automotive Radar Sensor Corner Case Simulation

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ushemadzoro Chipengo ◽  
Peter M. Krenz ◽  
Shawn Carpenter
Keyword(s):  
Target Identification ◽  
Pedestrian Detection ◽  
High Fidelity ◽  
Driver Assistance ◽  
Automotive Radar ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Radar Systems ◽  
False Target ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) have recently been thrust into the spotlight in the automotive industry as carmakers and technology companies pursue effective active safety systems and fully autonomous vehicles. Various sensors such as lidar (light detection and ranging), radar (radio detection and ranging), ultrasonic, and optical cameras are employed to provide situational awareness to vehicles in a highly dynamic environment. Radar has emerged as a primary sensor technology for both active/passive safety and comfort-advanced driver-assistance systems. Physically building and testing radar systems to demonstrate reliability is an expensive and time-consuming process. Simulation emerges as the most practical solution to designing and testing radar systems. This paper provides a complete, full physics simulation workflow for automotive radar using finite element method and asymptotic ray tracing electromagnetic solvers. The design and optimization of both transmitter and receiver antennas is presented. Antenna interaction with vehicle bumper and fascia is also investigated. A full physics-based radar scene corner case is modelled to obtain high-fidelity range-Doppler maps. Finally, this paper investigates the effects of inclined roads on late pedestrian detection and the effects of construction metal plate radar returns on false target identification. Possible solutions are suggested and validated. Results from this study show how pedestrian radar returns can be increased by over 16 dB for early detection along with a 27 dB reduction in road construction plate radar returns to suppress false target identification. Both solutions to the above corner cases can potentially save pedestrian lives and prevent future accidents.

scholarly journals Robust pedestrian detection for driver assistance systems using machine learning

2020 ◽  
Vol 83 (2/3/4) ◽  
pp. 140
Author(s):  
Sabrine Hamdi ◽  
Souhir Sghaier ◽  
Hassene Faiedh ◽  
Chokri Souani
Keyword(s):  
Machine Learning ◽  
Pedestrian Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems
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