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Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.

 

This work provides the design of a multifocal display that can create a dense stack of focal planes in a single shot. We achieve this using a novel computational lens that provides spatial selectivity in its focal length, i.e, the lens appears to have different focal lengths across points on a display behind it. This enables a multifocal display via an appropriate selection of the spatially-varying focal length, thereby avoiding time multiplexing techniques that are associated with traditional focus tunable lenses. The idea central to this design is a modification of a Lohmann lens, a focus tunable lens created with two cubic phase plates that translate relative to each other. Using optical relays and a phase spatial light modulator, we replace the physical translation of the cubic plates with an optical one, while simultaneously allowing for different pixels on the display to undergo different amounts of translations and, consequently, different focal lengths. We refer to this design as a Split-Lohmann multifocal display. Split-Lohmann displays provide a large étendue as well as high spatial and depth resolutions; the absence of time multiplexing and the extremely light computational footprint for content processing makes it suitable for video and interactive experiences. Using a lab prototype, we show results over a wide range of static, dynamic, and interactive 3D scenes, showcasing high visual quality over a large working range.

 

We exploit memory effect correlations in speckles for the imaging of incoherent fluorescent sources behind scattering tissue. These correlations are often weak when imaging thick scattering tissues and complex illumination patterns, both of which greatly limit the practicality of associated techniques. In this work, we introduce a spatial light modulator between the tissue sample and the imaging sensor and capture multiple modulations of the speckle pattern. We show that by correctly designing the modulation patterns and the associated reconstruction algorithm, statistical correlations in the measurements can be greatly enhanced. We exploit this to demonstrate the reconstruction of mega-pixel sized fluorescent patterns behind the scattering tissue.

 

Connected automated vehicles (CAVs), built upon advanced vehicle control and communication technology, can improve traffic throughput, safety, and energy efficiency. Previous studies on CAVs control focus on instability and stability properties of CAV platoons; however, these analyses cannot reveal the damping platoon oscillation characteristics, which are important for enhancing CAV platoon reliability against variant continuous perturbations. To this end, this research seeks to characterize the damping oscillations of CAVs through exploiting the platoon's unforced oscillatory, i.e., damping behavior. Inspired by the mechanical vibration theory, the proposed approach is applied to a CAV platoon with linear car-following control formulated as Helly's model and the predecessor-following communication topology. The proposed approach is applied to a CAV platoon with the linear car-following control formulated as Helly's model and the predecessor-following communication topology. Numerical analysis results show that a periodic perturbation with the resonance frequency of the CAV platoon will amplify the oscillation and lead to the severest oscillatory traffic. Our analysis highlights the importance of preventing platoon oscillations from resonance in ensuring CAV platooning reliability. 

Rapid advances in vehicle automation and communication technologies enable connected autonomous vehicles (CAVs) to cross intersections cooperatively, which could significantly improve traffic throughput and safety at intersections. Virtual platooning, designed upon car‐following behavior, is one of the promising control methods to promote cooperative intersection crossing of CAVs. Nevertheless, demand variation raises safety and stability concerns when CAVs adopt a virtual platooning control approach. Along this line, this study proposes an adaptive vehicle control method to facilitate the formation of a virtual platoon and the cooperative crossing of CAVs, factoring demand variations at an isolated intersection. This study derives the stability conditions of virtual CAV platoons depending on the time‐varying traffic demand. Based on the derived stability conditions, an optimization model is proposed to adaptively control CAVs dynamics by balancing approaching traffic mobility and safety to enhance the reliability of cooperative crossing at intersections. The simulation results show that, compared to the nonadaptive control, our proposed method can increase the intersection throughput by 18.2%. Also, time‐to‐collision results highlight the advantages of the proposed adaptive control in securing traffic safety.

 

Voice assistants such as Amazon Echo (Alexa) and Google Home use microphone arrays to estimate the angle of arrival (AoA) of the human voice. This paper focuses on adding user localization as a new capability to voice assistants. For any voice command, we desire Alexa to be able to localize the user inside the home. The core challenge is two-fold: (1) accurately estimating the AoAs of multipath echoes without the knowledge of the source signal, and (2) tracing back these AoAs to reverse triangulate the user's location.We develop VoLoc, a system that proposes an iterative align-and-cancel algorithm for improved multipath AoA estimation, followed by an error-minimization technique to estimate the geometry of a nearby wall reflection. The AoAs and geometric parameters of the nearby wall are then fused to reveal the user's location. Under modest assumptions, we report localization accuracy of 0.44 m across different rooms, clutter, and user/microphone locations. VoLoc runs in near real-time but needs to hear around 15 voice commands before becoming operational.