PROJECT OVERVIEW

We envision the need for a novel and completely collaborative approach for safer, more efficient and eco-friendly commutes in addition to current day V2V propositions. Real-time streaming data from multiple participants - vehicles, pedestrians and smart street IoTs (like smart traffic lights and pedestrian crossings) - when aggregated, fused and analyzed can provide an accurate, complete and dynamic street situational awareness to all the street participants, leading to collaborative and personalized guidance and advanced warnings services. As part of this project we are developing algorithms to accurately fuse data from multiple sources as well as determine the state of mind and intent of drivers inside vehicles. To enable this collaborative platform, we are developing reliable mmWave V2X communications techniques and enhanced edge computing resource allocation algorithms. Underlying all of our research is a vehicular and roadway testbed that supports data collection and validation of the developed techniques both on the UCSD campus and deployed alongside our city partners in the future.

Collaborative Intel

Connected and Autonomous Vehicles

Collaborative Intelligence in Smart and Connected Vehicles

Principal Investigator
Sujit Dey
Co-Principal Investigators
Dinesh Bharadia, Truong Nguyen
Research Students
Sam Thornton, Ji Dai, Kshitiz Bansal, Hao Le, Benjamin Chang, Joseph Chang, Runfa Li, Shiwei Jin

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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Collaborative Intel

Connected and Autonomous Vehicles

Driver State of Mind, Intent and Anomaly Detection using Multi-Modal Data

Principal Investigator
Sujit Dey
Research Students
Jianrong Chen

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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Vehicular Edge Computing

Connected and Autonomous Vehicles

Vehicular and Edge Computing for Emerging Connected and Autonomous Vehicle Applications

Principal Investigator
Sujit Dey
Research Students
Yujen Ku

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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Nested array

Connected and Autonomous Vehicles

Channel Modeling and Transceiver Architectures in Support of High Data Rate Communications in Mobile Environments

Principal Investigator
Bhaskar D. Rao
Research Students
Aditya Sant, Rohan Pote

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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Site-Specific_mmWave

Connected and Autonomous Vehicles

Site-Specific mmWave Channel Profiling, Prediction, and Network Adaptation

Principal Investigator
Sujit Dey
Co-Principal Investigators
Xinyu Zhang
Research Students
Bryse Flowers, Song Wang

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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Smart Transportation Testbed

Connected and Autonomous Vehicles

Smart Transportation Testbed

Principal Investigator
Sujit Dey
Co-Principal Investigators
Dinesh Bharadia, Xinyu Zhang
Co-Investigators
Sabur Baidya, PhD

We are developing an experimental testbed to explore hybrid vehicular-to-everything (V2X)communication and computing systems. Two experimental vehicles are currently instrumented with multiple sensors and hybrid wireless communication technologies including sub-6 GHz and mmWave radios mounted on the top rack or inside of the car. Each vehicle contains Onboard Units (OBUs), to support sensor data acquisition and local computing. Multiple Road-Side Units (RSUs), with overlapping coverage, consisting of sub-6 GHz and mmWave radios along with edge-computing nodes, and street IoTs are being deployed as well. The testbed enables real-world data collection for training AI models along with evaluation of the performance of the developed algorithms using the real world latency, computation, and bandwidth needs of multiple vehicles and roadway users.

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