Audition ability to enhance reliability of autonomous vehicles: Allowing cars to hear

The reliability of autonomous vehicles can be enhanced by providing the vehicle with more information about the surrounding environment. Autonomous vehicles typically use LiDAR, Radar, and computer vision to substitute for the driver’s vision. By adding auditory perception, an autonomous vehicle will improve its reliability and enable the vehicle to react better to the environment. This paper proposes a novel approach to enhance the reliability of autonomous vehicles. By adding auditory perception, the vehicle will be able to hear and process audio. To illustrate the advantage of auditory capability, we conducted an experiment to collect and process audio signals obtained from a vehicle driving on the road. We showed how an audio signal can be processed to obtain extra information that can alert the vehicle to potential dangers.Conference PaperPublishe

Machine learning to classify driving events using mobile phone sensors data

With the introduction of autonomous and self-driving cars, innovative research is needed to ensure safety and reliability on the road. This work introduces a solution to understand vehicle behaviour based on sensors data. The behaviour is classified according to driving events. Understanding driving events can play a significant role in road safety and estimating the expense and risks of driving a vehicle. Rather than relying on the distance and time driven, driving events can provide a more accurate measure of vehicle driving consumption. This measure will become valuable as more ride-sharing applications are introduced to roads around the world. Estimating driving events can also help better design the road infrastructure to reduce congestion, energy consumption and pollution. By sharing data from official vehicles and volunteers, crowd sensing can be used to better understand congestion and road safety. This work studies driving events and proposes using machine learning to classify these events into different categories. The acquired data is collected using embedded mobile device motion sensors to train machine learning algorithms to classify the events.Journal ArticleFinal article publishe

Data collection and generation for radio frequency signal security

The current proliferation of unmanned aerial systems (UASs) for a wide range of applications ranging from commercial to defence purposes demands the need for their protection. The development of security tools and techniques will need realistic radio frequency (RF) datasets for research and testing. This chapter presents an on-going research and development effort to produce RF signal datasets that can be used for the development and testing of machine learning (ML) systems. We envision that these systems will ultimately be the precursor of future autonomous and secure UAS to benefit society for many generations.Conference Pape