Real-time pedestrian detection for driver assistance systems based on deep learning

2018 ◽  
Author(s):  
Zhenfei Gong ◽  
Wenbing Tao ◽  
Xinyu Wang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Pedestrian Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpa Gite ◽  
Ketan Kotecha ◽  
Gheorghita Ghinea
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Real Time ◽  
Complete Analysis ◽  
Context Aware ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Content Type ◽  
Assistance Systems

Purpose This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques. Design/methodology/approach Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability. Findings There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains. Research limitations/implications The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers. Social implications As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians. Originality/value This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

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.

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