Nelms Institute Contact: Swarup Bhunia
With rising concerns over fossil fuel depletion and the impact of Internal Combustion Engine (ICE) vehicles on our climate, the transportation industry is observing a rapid proliferation of Electric Vehicles (EVs). Yet, people continue to use ICE vehicles over EVs due to consumer worries over issues such as limited range, limited battery life, long charging times, and the lack of EV charging stations. Existing solutions to these problems, such as building more charging stations, increasing battery capacity, and road-charging have not been proven efficient so far. In this paper, we propose Peer-to-Peer Car Charging (P2C2), a highly scalable novel technique for charging EVs on-the-go with minimal cost overhead. We allow EVs to share charge among each other based on the instructions from a cloud-based control system. The control system assigns and guides EVs for charge sharing. We also introduce Mobile Charging Stations (MoCS), which are high battery capacity vehicles that are used to replenish the overall charge in the vehicle networks. We have implemented P2C2 and integrated it with the traffic simulator, SUMO. We observe promising results with up to 65% reduction in the number of EV halts and with up to 24.4% reduction in required battery capacity without any extra halts.

Nelms Institute Contact: Lili Du
Emerging connected and autonomous vehicle (CAV) technologies offer great potentials to reduce traffic congestion and improve traffic efficiency. However, much of the CA related work focuses on individual vehicles’ safety, which compromises traffic efficiency when mixed traffic (CAVs and human-driven vehicles) is on the road interacting with each other. This project aims to study how a group of CAVs can respond to exogenous disturbances resulting from human-driven vehicles, lane change requests and abnormal traffic and cyber conditions through cooperative speed or acceleration control. The research will improve road safety and traffic efficiency of future transportation systems involving CAVs. This project will disseminate research and education outcomes to broader audiences, including under-represented college and K-12 students with a particular focus on minority students. The specific research objectives of this project are to develop vehicle platoon centered optimal, adaptive, and resilient vehicle platooning control under various normal or abnormal traffic and/or cyber conditions. This project will develop (a) advanced model predictive control integrating distributed optimization for optimal vehicle platooning control under normal traffic/cyber conditions; (b) mixed integer programming based model predictive control for optimal vehicle platooning control adaptive to lane change requests; (c) resilient vehicle platooning control integrating real-time learning and distributed optimization under abnormal traffic and/or cyber conditions.

Nelms Institute Contact: Lili Du
Traffic congestion jeopardizes the function of urban transportation systems and has a growing negative effect on the health of urban economies. It also increases air pollution with numerous negative health impacts on our citizenry. A promising solution to alleviating traffic congestion is to establish coordinated driving mechanisms. This is enabled by recent connected or even autonomous vehicle technologies and advanced onboard computing facilities. However, engineers who design such mechanisms are still lacking scientific knowledge and effective tools that can be proven as efficient and reliable for use by the general public. The goal of this Faculty Early Career Development (CAREER) program award is to develop innovative approaches to the coordination of connected vehicle drivers- online route choices. This will be done by exploiting emerging information and computing technologies equipped in connected transportation infrastructure. This approach will improve transportation system mobility, safety, and environmental sustainability without sacrificing the interests of the individual vehicles. This research will deepen our under- standing of the competition among vehicles on limited traffic resources. It should also reveal the impacts of the decisions of individual vehicles on traffic congestion, and offer a new paradigm of real-time traffic control.

Nelms Institute Contact: Yuguang “Michael” Fang
This one-year project aims to demonstrate how to leverage light-weight vehicles equipped with powerful cognitive radio (CR) routers with high computational capability and relatively large storage capacity to perform spectrum sensing, processing and storing data, making intelligent decisions, and opportunistically transporting data for emerging IoT systems and smart cities applications. To achieve this goal, the project plans to design a flexible and agile cognitive network architecture to effectively take advantage of the added capability in vehicles. Under this architecture, a suite of spectrum management mechanisms will be developed from both access point of view and end-to-end service perspective. Delay-tolerant traffic will shift to this emerging network where harvested licensed or unlicensed spectrum can be used to opportunistically store carry-forward data traffic. By designing various kinds of opportunistic data offloading mechanisms, the project seeks to explore the effectiveness of such a data transportation network.

Nelms Institute Contact: Sandip Ray
How would you feel if a hacker could remotely push a button that would cause your vehicle to veer off the highway into a ditch? Research over the last decade has shown that not only is this possible but it is actually depressingly easy for a trained hacker to do so. The reason is that as vehicles get infused with electronics and software to support and create various autonomous features, they are starting to look more like computers than as traditional cars. That also means that they are inheriting the problems that have plagued computers for decades – cyber-security. The only difference in this case is that vehicles are more like computers driving at 70 miles/hour and with people inside. Cyber-attacks on these systems can cause catastrophic accidents, cost human life, and bring down transportation infrastructure. As we increase autonomous features of vehicles and move toward self-driving cars, we are sorely in need for a robust vehicular design that is resilient to cyber-attacks. A key feature of emergent vehicles is connectivity. Vehicles can “talk” to other vehicles as well as with the transportation infrastructure through sensors and inter-vehicular communications (called V2X) to enable smooth and efficient traffic flow and infrastructure utilization. Connected autonomous vehicle (CAV) applications are designed today include platooning, cooperative collision detection, cooperative on-ramp merging, etc. Connectivity, however, is also one of the most vulnerable components of autonomous vehicles and one of the crucial entry points for cyber-attacks. A key feature of such attacks is that they can be conducted without requiring an adversary to actually hack into the hardware/software or physical components of the target vehicle. They can simply send misleading or even malformed communications to “confuse” the communication or sensor systems. The goal of this project is o address this crucial problem of cyber-resiliency of CAV applications. A key result from the team is a unique AI-based resiliency architecture against arbitrary cyber-attacks on perception channels (e.g., communication and sensor channels). To our knowledge this is the first (and so far, the only) comprehensive resiliency framework for connected vehicle applications against arbitrary cyber-attacks. The architecture exploits recent advances in AI and machine learning to create a unique, on-board predictor to detect, identify, and respond to malicious communications and sensory subversions. A unique feature of the approach is that it can provide assured resiliency against a large class of adversaries, including unknown attacks. We have instantiated the approach on several CAV applications and developed an extensive experimental evaluation methodology for demonstrating such resiliency.

Nelms Institute Contact: Shuo Wang
Modern high-speed variable motor drive systems are widely used in the electrification of transportation systems. These systems include both small-signal and high power systems. The high power system generates EM radiation which can induce unwanted noise in the small-signal systems. The noise can lead to system malfunction causing safety and reliability issues. Identify- ing the EMI radiation sources, understanding the radiation mechanism, and developing suppression techniques are extremely important in the applications of electrification of transportation.

Nelms Institute Contact: Shuo Wang
DC/DC power conversion is very popular in automotive applications. Some of the examples are the drivers of LED lighting and the auxiliary power supplies for the digital signal processing (DSP) control unit. The EM radiation can be generated from inappropriate PCB layouts, commercial magnetic components, undesired cable antennas, and unwanted near-field couplings. Investigating the mechanism of the EM radiation and optimizing PCB layout and magnetic component design can greatly reduce the radiated EM interference.

Nelms Institute Contact: Shuo Wang
EM radiation from electronics circuits such as IC and power converters is troublesome. Attackers can use EM side-channel leakage techniques to procure the information of the ICs and energy conversion systems to launch possible malicious attacks. The EM radiation is related to the internal power delivery layouts of ICs and outer PCB layouts. By understanding the EM radiation generated by the layouts, one can minimize the EM radiation, mitigate the risk of EM side-channel leakage-related attack, and improve the safety of the systems. Optimal shielding techniques can also be implemented to both IC packaging level and circuit structure level to minimize the risk of EM side-channel attacks.

Nelms Institute Contact: Shuo Wang
Time-domain EM radiation can be measured using EM probes and an oscilloscope. The measured EM signal can be analyzed for getting useful information. On the other hand, the EM signals have their special spectrum signatures. By analyzing the relationship of time-domain EMI signal and frequency domain spectrum, one can predict the EM spectrum and decipher useful information from the spectrum which is impossible from the analysis of time-domain signals.

Nelms Institute Contact: Shuo Wang
Modern power electronics demand high power density and high-performance power semicon- ductor devices. The packaging techniques play a big role in the power semiconductor’s performance. It is found that not only self but also mutual parasitic inductance in the packaging layout play an important role in semiconductor’s switching and EM radiation behavior. A novel packaging technique is being developed to drastically reduce the parasitics and therefore reduce parasitic voltage ringings, reduce switching power loss, reduce thermal stress, reduce EM radiation and improve the reliability and power density of the semiconductor devices.

Nelms Institute Contact: Shuo Wang
Transforming gate-level circuit designs into hypergraphs allow for more efficient analysis and sub-division. Specifically partitioning hypergraphs into several small subgraph reduces work load when inserting configurable LUTs and programmable switchboxes and interconnects in the design. This allows traditional Designs to be more readily implemented in FPGAs and other reconfigurable logic devices, and potentially reduces overheads when doing so. Reducing overheads is especially pertinent when developing solutions for IoT applications.

Nelms Institute Contact: Shuo Wang
Due to their high switching speed, SiC and GaN wide-bandgap devices are more and more popular in high-frequency energy conversion systems. However, high speed leads to high radiated near field and far-field EM radiation. The EM radiation can contaminate the digital and control circuits nearby causing safety and reliability issues. The relationship between the switching characteristics and EM radiation is being investigated and the EM model is being developed. Based on the model, EM radiation can be predicted and novel suppression techniques are being developed.

Nelms Institute Contact: Shuo Wang
Magnetic components such as inductors and transformers are widely used in power electronics, digital electronics, and small-signal analog circuit applications. These magnetic components can generate both near field and far field EM radiation which leads to both conductive and radiated electromagnetic interference (EMI). The EMI can violate FCC limits and causing safety and reliability troubles. Studying the impacts of winding shapes, magnetic materials, and switching voltages to the radiation can help to minimize the EM radiation and keep a clean EMI-free environment.

Nelms Institute Contact: Sanjeev Koppal
The light-transport matrix is a rich and complex representation of how light from an illumination source interacts with a scene and reaches the camera. Unfortunately, light transport matrices are huge; the light ray set is typically large and, further, the radiance quality along the rays (high dynamic range, color, etc.) implies a big data footprint. In this project, the researchers consider dynamic light-transport matrices for scenes with motion, where the size and throughput requirements are even higher and have inhibited previous work on capture, analysis and applications. They believe that breaking through these imaging challenges is useful because a dataset of dynamic light-transport matrices will allow the team to intricately unwrap complex interactions of light and objects in time, such as motion, occlusion as line-of sight changes, illumination due to lighting variation, secondary light paths such as specular interreflections and indirect/ global illumination of dynamic scenes. The full light-transport at each moment in time provides a complete picture of these dynamic interactions (allowing scene recovery by tracking and 3D scanning), with secondary light paths that offer robustness in the face of complex visual effects (allowing post-capture image-based relighting). The goal of this project is to fundamentally understand and characterize the properties of light transport for dynamic, moving objects.