Cooperative autonomous driving, which extends vehicle autonomy by enabling real-time collaboration between vehicles and smart roadside infrastructure, remains a challenging yet essential problem. However, none of the existing testbeds employ smart infrastructure equipped with sensing, edge computing, and communication capabilities. To address this gap, we design and implement a 1:15-scale miniature testbed, CIVAT, for validating cooperative autonomous driving, consisting of a scaled urban map, autonomous vehicles with onboard sensors, and smart infrastructure. The proposed testbed integrates V2V and V2I communication with the publish-subscribe pattern through a shared Wi-Fi and ROS2 framework, enabling information exchange between vehicles and infrastructure to realize cooperative driving functionality. As a case study, we validate the system through infrastructure-based perception and intersection management experiments.

As cities strive to address urban mobility challenges, combining autonomous transportation technologies with intelligent infrastructure presents an opportunity to transform how people move within urban environments. Autonomous shuttles are particularly suited for adaptive and responsive public transport for the first and last mile, connecting with smart infrastructure to enhance urban transit. This paper presents the concept, implementation, and evaluation of a proof-of-concept deployment of an autonomous shuttle integrated with smart infrastructure at a public fair. The infrastructure includes two perception-equipped bus stops and a connected pedestrian intersection, all linked through a central communication and control hub. Our key contributions include the development of a comprehensive system architecture for "smart" bus stops, the integration of multiple urban locations into a cohesive smart transport ecosystem, and the creation of adaptive shuttle behavior for automated driving. Additionally, we publish an open source dataset and a Vehicle-to-X (V2X) driver to support further research. Finally, we offer an outlook on future research directions and potential expansions of the demonstrated technologies and concepts.

This paper presents a 1/10th scale mini-city platform used as a testing bed for evaluating autonomous and connected vehicles. Using the mini-city platform, we can evaluate different driving scenarios including human-driven and autonomous driving. We provide a unique, visual feature-rich environment for evaluating computer vision methods. The conducted experiments utilize onboard sensors mounted on a robotic platform we built, allowing them to navigate in a controlled real-world urban environment. The designed city is occupied by cars, stop signs, a variety of residential and business buildings, and complex intersections mimicking an urban area. Furthermore, We have designed an intelligent infrastructure at one of the intersections in the city which helps safer and more efficient navigation in the presence of multiple cars and pedestrians. We have used the mini-city platform for the analysis of three different applications: city mapping, depth estimation in challenging occluded environments, and smart infrastructure for connected vehicles. Our smart infrastructure is among the first to develop and evaluate Vehicle-to-Infrastructure (V2I) communication at intersections. The intersection-related result shows how inaccuracy in perception, including mapping and localization, can affect safety. The proposed mini-city platform can be considered as a baseline environment for developing research and education in intelligent transportation systems.

How does cloudy weather affect a grid powered by solar energy? How do we ensure that electricity is delivered to the consumer if a grid is powered by wind and the wind does not blow? What's the best course of action if a bird hits a plane engine on takeoff? How can you predict the behavior of a cyber attacker? A senior research scientist in MIT's Department of Mechanical Engineering, Annaswamy spends most of her research time dealing with decision-making under uncertainty.

Real-time traffic signal control presents a challenging multiagent planning pro blem, particularly in urban road networks where, unlike simpler arterial settings, there are competing dominant traffic flows that shift through the day. Further complicating matters, urban environments require attention to multimodal traffic flows (vehicles, pedestrians, bicyclists, buses) that move at different speeds and may be given different priorities. For the past several years, my research group has been developing and refining a real-time, adaptive traffic signal control system to address these challenges, referred to as scalable urban traffic control (Surtrac). Combining principles from automated planning and scheduling, multiagent systems, and traffic theory, Surtrac treats traffic signal control as a decentralized online planning process. In operation, each intersection repeatedly generates and executes (in rolling horizon fashion) signal-timing plans that optimize the movement of currently sensed approaching traffic through the intersection.

The idea of self-driving cars has captured consumer imagination, but AI is having a much broader impact across the entire automotive industry. From design, manufacturing, and infrastructure to predictive maintenance, safety, and a slew of AI-enabled cockpit features, auto experiences are evolving and improving. Not surprising, on-device AI is the primary force driving this transformation. It's enabling compute-intense AI workloads, such as complex neural network models, to execute in real time with high accuracy. In addition, as chipset providers add advanced AI capabilities and waterfall them across product tiers, sophisticated use cases are not just for luxury vehicles, but for entry-level cars as well.

While artificial intelligence is a loaded term that for some may conjure up images of a malicious Skynet system from the Terminator movie franchise, the reality is not as ominous. And when Sen. Ted Cruz, R-Texas, argued during the U.S. Congress' first AI hearing -- dubbed "The Dawn of Artificial Intelligence" -- that it is already at work in the United States, improving the efficiency and productivity of systems across the map, he was right. "Artificial intelligence is already seeping into our daily lives," said Cruz, who is chairman of the Senate subcommittee on Space, Science and Competitiveness. The hearing, which included representatives from Microsoft, Carnegie Mellon University and NASA, among others, focused on the potential implications machine learning will have on the country's labor market, national security and transportation. One of the largest areas for growth through artificial intelligence is smart city planning and smart infrastructure.