Advanced Signal Processing Algorithms for Security and Safety in Vehicular Applications

Abstract

Advanced driver assistance systems (ADAS) have witnessed a significant growth in the last decade. To complement the ADAS technologies, wireless vehicular communications emerged as a promising solution providing vehicles with an all-directions non-line of sight view of their surroundings. This dissertation focuses on the security aspects of vehicular communications in addition to in-vehicle safety aspects of ADAS applications. Wireless communications are vulnerable to potential security attacks due to their broadcast nature. Vehicles exchange critical safety information about their speed, direction and location which necessities efficient algorithms to secure the ongoing communications. This motivates us to investigate the physical layer (PHY) security for wireless systems having fast fading channels, few transmit antennas and stringent transmit power constraints as imposed by the vehicular communication standards. We study the PHY security in a single-user orthogonal frequency-division multiplexing (OFDM) and multi-user single-carrier frequency-division multiple-access (SC-FDMA) systems where transmitter(s), denoted by Alice(s), transmit confidential data messages to a neighbouring receiver, which we refer to as Bob, in the presence of eavesdropping node(s), denoted by Eve(s). Alice(s) send a temporal artificial-noise (AN) signal superimposed over their information signal to secure the ongoing communications. We investigate the impact of the channel delay spread, cyclic prefix, information/AN signals power allocations, channel state information (CSI) knowledge and information and AN precoders design on the achievable average secrecy rate in single-user and multi-users systems. We highlight the trade-offs between the computational complexity and the average secrecy rate performance for different detection strategies. In addition, we enhance the system’s security by proposing a hybrid scheme that combines temporal AN with channel-based secret-key extraction. Furthermore, we propose a novel secure scheme that utilizes the sub-channel orthogonality of OFDM systems to simultaneously transmit data and secret key symbols in two opposite directions. Bob, shares secret key symbols with Alice using wiretap coding over a portion of the sub-channels. Simultaneously, Alice uses the accumulated secret keys in her secret-key queue to encrypt data symbols using a one time pad (OTP) cipher and transmits them to Bob over the remaining sub-channels. We drive closed-form expressions for the average secrecy rate and the secrecy outage probabilities for the different proposed schemes. In the second part of this dissertation, we focus on the in-vehicle safety aspects of ADAS applications with emphasis on the driver’s gaze estimation which is essential to asses the visual attention of drivers allowing alarming drivers in different distraction scenarios. We present a 3D head based coordinate system whose center is the driver’s head center of mass (CoM). Then, we utilize fiducial markers to precisely annotate the driver’s gaze into elevation and azimuth angles. We increase the realism of the discrete driving data by augmenting it to continuous gaze data collected while the car is parked. Then, we propose deep-learning based gaze estimation algorithms that utilizes all features on the driver’s face to produce new statistical customizable gaze regions estimates. Our statistical gaze estimations provide the level of confidence in estimation which can be used a useful parameter for different ADAS applications. We perform large scale cross-driver testing utilizing the data from 54 different subjects collected in naturalistic driving environments.