AI-based Pedestrian Detection and Avoidance at Night using an IR Camera, Radar, and a Video Camera

In 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentially fatal accidents involving pedestrians crossing a street. The working prototype of pedestrian detection and collision avoidance can be installed in present-day vehicles, with the visible camera used to detect pedestrians during the day and the infrared camera to detect pedestrians primarily during the night as well as at high glare from the sun during the day. The radar sensor is also used to detect the presence of a pedestrian and calculate their range and direction of motion relative to the vehicle. Through data fusion and deep learning, the ability to quickly analyze and classify a pedestrian’s presence at all times in a real-time monitoring system is achieved. The system can also be extended to cyclist and animal detection and avoidance, and could be deployed in an autonomous vehicle to assist in automatic braking systems (ABS)

A Visible Light Communications Framework for Intelligent Transportation Systems

In this work, we developed a visible light communication (VLC) framework that can be used for Intelligent Transportation Systems (ITS). ITS has been motivated by the need for reducing traffic congestion and offering better user experience in navigation and location-specific services. Recently, VLC has drawn a great deal of attention in the research community, including the development of new applications for ITS. It would be of great use to enable the traffic lights to be able to talk to the vehicles in their proximity and convey important information about the traffic condition. In this project, we developed a framework that can potentially support infrastructure-to-vehicle (I2V) and vehicle-to-infrastructure (V2I) communication. (In our context the infrastructure refers to traffic lights using VLC.) Specifically, traffic lights will be used to not only to order traffic flow, but also to share some important information to the cars. The developed smart traffic light system can provide information about the traffic conditions several blocks down the road and, in case of accidents, this information would be useful for the driver to detour their original route to help reduce congestion and save time. In order to do that we have developed a transmitter circuitry that is composed of an embedded system and optical electronics. In addition, we have developed the receiver circuitry in which the photodiode along with other circuitry is used for detecting and decoding the VLC signal coming from the traffic lights. We have also developed and experimented in a laboratory with a novel optical code-division multiple-access (CDMA) scheme for overloaded optical CDMA transmission in which the optical codes are uniquely decodable. This new coding system could potentially provide higher data rate in the VLC protocol establishment

Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning

According to the National Highway Traffic Safety Administration, in 2017 drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, as well as almost 800 deaths. Through the application of visual and radar sensors combined with machine learning, this research developed a drowsy driver detection system aimed to prevent potentially fatal accidents. The working prototype of Advanced Driver Assistance Systems can be installed in present-day vehicles to detect drowsy drivers with over 95% accuracy. It integrates two types of visual surveillance to examine the driver for signs of drowsiness. A camera is used to monitor the driver’s eyes, mouth and head movement in order to recognize when a discrepancy occurs in the driver\u27s eye blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor in the system allows the driver\u27s head movement to be captured at all times. Through data fusion and deep learning, the system quickly analyzes and classifies a driver\u27s behavior under various conditions in real-time monitoring. This research could be implemented to reduce drowsy driving, thereby, making the roads safer for everyone and ultimately saving lives

Uniquely Decodable Ternary Codes for Synchronous CDMA Systems

In this paper, we consider the problem of recursively designing uniquely decodable ternary code sets for highly overloaded synchronous code-division multiple-access (CDMA) systems. The proposed code set achieves larger number of users $K < K_{max}^t$ than any other known state-of-the-art ternary codes that offer low-complexity decoders in the noisy transmission. Moreover, we propose a simple decoder that uses only a few comparisons and can allow the user to uniquely recover the information bits. Compared to maximum likelihood (ML) decoder, which has a high computational complexity for even moderate code length, the proposed decoder has much lower computational complexity. We also derived the computational complexity of the proposed recursive decoder analytically. Simulation results show that the performance of the proposed decoder is almost as good as the ML decoder.Comment: arXiv admin note: text overlap with arXiv:1806.0395

Fast Decoder for Overloaded Uniquely Decodable Synchronous Optical CDMA

In this paper, we propose a fast decoder algorithm for uniquely decodable (errorless) code sets for overloaded synchronous optical code-division multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a way that the users can uniquely recover the information bits with a very simple decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML) decoder, which has a high computational complexity for even moderate code lengths, the proposed decoder has much lower computational complexity. Simulation results in terms of bit error rate (BER) demonstrate that the performance of the proposed decoder for a given BER requires only 1-2 dB higher signal-to-noise ratio (SNR) than the ML decoder.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0395

Fast Decoder for Overloaded Uniquely Decodable Synchronous CDMA

We consider the problem of designing a fast decoder for antipodal uniquely decodable (errorless) code sets for overloaded synchronous code-division multiple access (CDMA) systems where the number of signals K_{max}^a is the largest known for the given code length L. The proposed decoder is designed in a such a way that the users can uniquely recover the information bits with a very simple decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML) decoder, which has a high computational complexity for even moderate code length, the proposed decoder has a much lower computational complexity. Simulation results in terms of bit error rate (BER) demonstrate that the performance of the proposed decoder only has a 1-2 dB degradation at BER of 10^{-3} when compared to ML

AI-Based Pedestrian Detection and Avoidance at Night Using an IR Camera, Radar, and a Video Camera

ZSB12017-SJAUXIn 2019, the United States experienced more than 6,500 pedestrian fatalities involving motor vehicles which resulted in a 67% rise in nighttime pedestrian fatalities and only a 10% rise in daytime pedestrian fatalities. In an effort to reduce fatalities, this research developed a pedestrian detection and alert system through the application of a visual camera, infrared camera, and radar sensors combined with machine learning. The research team designed the system concept to achieve a high level of accuracy in pedestrian detection and avoidance during both the day and at night to avoid potentially fatal accidents involving pedestrians crossing a street. The working prototype of pedestrian detection and collision avoidance can be installed in present day vehicles, with the visible camera used to detect pedestrians during the day and the infrared camera to detect pedestrians primarily during the night as well as at high glare from the sun during the day. The radar sensor is also used to detect the presence of a pedestrian and calculate their range and direction of motion relative to the vehicle. Through data fusion and deep learning, the ability to quickly analyze and classify a pedestrian\u2019s presence at all times in a real-time monitoring system is achieved. The system can also be extended to cyclist and animal detection and avoidance, and could be deployed in an autonomous vehicle to assist in automatic braking systems (ABS)

Multiway Physical-Layer Network Coding via Uniquely Decodable Codes

We focus on a multiway relay channel (MWRC) network where two or more users simultaneously exchange information with each other through the help of a relay node. We propose for the first time to apply ternary uniquely decodable (UD) code sets that we have developed to allow each user to uniquely recover the information bits from the noisy channel environment. One of the key features of the proposed scheme is that it utilizes a very simple decoding algorithm, which requires only a few logical comparisons. Simulation results in terms of bit error rate (BER) demonstrate that the performance of the proposed decoder is almost as good as the maximum-likelihood (ML) decoder. In addition to that through simulations, we show that the proposed scheme can significantly improve the sum-rate capacity, which in turn can potentially improve overall throughput, as it needs only two time slots (TSs) to exchange information compared to the conventional methods

Low-density spreading codes for NOMA systems and a Gaussian separability based design

Improved low-density spreading (LDS) code designs based on the Gaussian separability criterion are conceived. We show that the bit-error-rate (BER) hinges not only on the minimum distance criterion, but also on the average Gaussian separability margin. If two code sets have the same minimum distance, the code set having the highest Gaussian separability margin will lead to a lower BER. Based on the latter criterion, we develop an iterative algorithm that converges to the best known solution having the lowest BER. Our improved LDS code set outperforms the existing LDS designs in terms of its BER performance both for binary phase-shift keying (BPSK) and for quadrature amplitude modulation (QAM) transmissions. Furthermore, we use an appallingly low-complexity minimum mean-square estimation (MMSE) and parallel interference cancellation (PIC) (MMSE-PIC) technique, which is shown to have comparable BER performance to the potentially excessive-complexity maximum-likelihood (ML) detector.This MMSE-PIC algorithm has a much lower computational complexity than the message passingalgorithm (MPA)