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A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behavior and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions, and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the multimodal driver monitoring (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi-Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver's facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver's activities are recorded with high definition RGB cameras and a time-of-flight depth camera. We also record the controller area network-bus (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.

We investigate the physical layer security of uplink single-carrier frequency-division multipleaccess (SC-FDMA) systems. Multiple users, Alices, send confidential messages to a common legitimate base-station, Bob, in the presence of an eavesdropper, Eve. To secure the legitimate transmissions, each user superimposes an artificial noise (AN) signal on the time-domain SC-FDMA data block.We reduce the computational and storage requirements at Bob's receiver by assuming simple per-subchannel detectors. We assume that Eve has global channel knowledge of all links in addition to high computational capabilities, where she adopts high-complexity detectors such as single-user maximum likelihood (ML), multiuser minimum-mean-square-error (MMSE), and multiuser ML. We analyze the correlation properties of the time-domain AN signal and illustrate how Eve can exploit them to reduce the AN effects. We prove that the number of useful AN streams that can degrade Eves signal-to-noise ratio (SNR) is dependent on the channel memories of Alices-Bob and Alices-Eve links. Furthermore, we enhance the system security for the case of partial Alices-Bob channel knowledge at Eve, where Eve only knows the precoding matrices of the data and AN signals instead of knowing the entire Alices-Bob channel matrices, and propose a hybrid scheme that integrates temporal AN with channel-based secret-key extraction.

We investigate the physical layer security of wireless single-input single-output orthogonal-division multiplexing (OFDM) when a transmitter, which we refer to as Alice, sends her information to a receiver, which we refer to as Bob, in the presence of an eavesdropping node, Eve. To prevent information leakage, Alice sends an artificial-noise (AN) signal superimposed over her information signal. We investigate the impact of the channel delay spread, OFDM cyclic prefix, information/AN power allocation, and information and AN precoders design on the achievable average secrecy rate. We consider the two cases of known and unknown channel state information (CSI) at Alice. Furthermore, we compare both cases of per-sub-channel processing and joint sub-channels processing at Eve's receiver. Our numerical results show the gains of AN injection in terms of average secrecy rate for different OFDM operating conditions. Moreover, based on our new insights, we demonstrate that the AN-aided scheme is effective and achieves almost the same average secrecy rate as the full-CSI case without the need for Eve's instantaneous CSI at Alice.

We propose a new scheme to enhance the physicallayer security of wireless single-input single-output orthogonalfrequency division-multiplexing (OFDM) transmissions from an electric vehicle, Alice, to the aggregator, Bob, in the presence of an eavesdropper, Eve. To prevent information leakage to Eve, Alice exploits the wireless channel randomness to extract secret key symbols that are used to encrypt some data symbols which are then multiplexed in the frequency domain with the remaining unencrypted data symbols. To secure the unencrypted data symbols, Alice transmits an artificial-noise (AN) signal superimposed over her data signal. We propose a three-level optimization procedure to increase the average secrecy rate of this wiretap channel by optimizing the transmit power allocation between the encrypted data symbols, unencrypted data symbols and the AN symbols. Our numerical results show that the proposed scheme achieves considerable secrecy rate gains compared to the benchmark cases.

We investigate the physical-layer security of indoor hybrid parallel power-line/wireless orthogonal-frequency division-multiplexing (OFDM) communication systems. We propose an artificial-noise (AN) aided scheme to enhance the system's security in the presence of an eavesdropper by exploiting the decoupled nature of the power-line and wireless communication media. The proposed scheme does not require the instantaneous channel state information of the eavesdropper's links to be known at the legitimate nodes. In our proposed scheme, the legitimate transmitter (Alice) and the legitimate receiver (Bob) cooperate to secure the hybrid system where an AN signal is shared from Bob to Alice on the link with the lower channel-to-noise ratio (CNR) while the information stream in addition to a noisy-amplified version of the received AN signal is transmitted from Alice to Bob on the link with higher CNR at each OFDM sub-channel. In addition, we investigate the effect of the transmit power levels at both Alice and Bob and the power allocation ratio between the data and AN signals at Alice on the secure throughput. We investigate both single-link eavesdropping attacks, where only one link is exposed to eavesdropping attacks, and twolink eavesdropping attacks, where the two links are exposed to eavesdropping attacks.Index Terms-Wiretap channel, hybrid systems, artificial noise Eve Wireless OFDM Receiver Bob Wireless and PLC OFDM Receiver Alice Wireless and PLC Transmitter (a) Single-link eavesdropping: eavesdropping on wireless link Eve PLC OFDM Receiver Bob Wireless and PLC OFDM Receiver Alice Wireless and PLC Transmitter (b) Single-link eavesdropping: eavesdropping on PLC link Eve 2 PLC OFDM Receiver Bob Wireless and PLC OFDM Receiver Alice Wireless and PLC Transmitter

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