Improvements in vehicular perception systems over the last decade have enabled new levels of safety and awareness in modern production vehicles. However, achievable performance of these perception systems is bounded by sensor limitations, such as range, and environmental factors, such as occlusion. Collaborative perception circumvents these limitations by incorporating sensor data from multiple sources to fill in perception gaps experienced by an individual sources’ sensors.

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The current Cellular Vehicle-to-Everything (C-V2X) Sidelink communication protocol provides a low latency interface for sharing short safety messages among Road-Side Units (RSUs) and On-Board Units (OBUs). However, the protocol is primarily designed for broadcasting small messages and lacks link adaptation capabilities – this project utilizes AI/ML techniques to augment C-V2X Sidelinks with this adaptation capability in order to enable emerging applications such as vehicular sensor fusion.

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While Connected Vehicles can share their locations and plans with one another, it is challenging to ask other road users such as pedestrians or cyclists to do the same. Moreover, not all vehicles will be capable of this collaboration as some vehicles, especially older ones, will not have sufficient technology. We are examining ways for Connected Vehicles, along with the deployed infrastructure, to collaboratively perceive the environment in order to create exceptionally accurate and thorough roadway awareness. This awareness will allow for better path planning to be undertaken by Autonomous Vehicles, humans through the usage of alerts and HUDs, and infrastructure through traffic and pedestrian management.

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In this project, we plan to use multi-modal data from sensors such as 3D and infrared cameras, wearable smartwatch, in-vehicle telematics devices, together with new multi-modal data fusion, machine vision and artificial intelligence techniques, to identify accurately the driver’s state of mind (SoM) and state of health (SoH), driving anomaly, as well as his/her intent. A system diagram is shown in Figure 1. A driver’s state of mind can consist of emotions like anger, anxiety and fear, as well as indications of distraction and fatigue; driver’s intent can consist of future driving actions like lane changing, turning, and overtaking; and driving anomaly can consist of unexpected or undesirable driving behaviors like sudden accelerating or decelerating, weaving, and wild turns. While cameras are widely used in driver monitoring systems, accurate detection may still be a challenge due to various limitations such as lighting conditions and driver’s poses.

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Emerging connected and autonomous vehicles involve complex applications requiring not only optimal computing resource allocations but also efficient computing architectures. We investigate the critical performance metrics required for emerging vehicular computing applications and develop algorithms for optimal choices to satisfy the static and dynamic computing requirements in terms of the performance metrics. Employing the edge computing architectures for vehicular computing, we are developing different task partitioning and task offloading strategies. 

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The project will examine massive MIMO communication to support the high data rate needs of autonomous driving. Both sub 6 Ghz and mm-Wave communication will be of interest with some preference to mm-Wave communication. The specific aims of our project are: 1) Development and analysis of low complexity receiver and transmitter architectures (mm-Wave) in support of the communication needs. In particular, development of suitable antenna array architectures along with the RF chains topology for rich channel sensing. 2) Novel channel sensing methods that are synergistic with the architectures and development of channel estimation techniques that enable a richer characterization of the communication environment than hitherto possible. 3) Novel channel models to incorporate mobility as well as the development of algorithms to track the channel in mobile environments.

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Usage of the mmWave frequencies is a key driver towards improving cellular network capacity for 5G and beyond. However, most of the current use cases are limited to static point-to-point settings, such as WirelessHD and Integrated Access and Backhaul. In contrast, anticipated 5G applications like immersive media and V2X will require mmWave to deliver LTE-like coverage, stability, and mobility support. The main challenge to meet this prospect lies in the fact that mmWave link performance becomes a sensitive function of the environment, due to strong attenuation, blockage, and reflection.

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