The UC Berkeley Institute of Transportation Studies showed off recent innovations in the transportation ecosphere involving artificial intelligence to over 100 attendees at the DRIVE AI (Data · Robotics · Infrastructure · Vehicles · Electrification · Artificial Intelligence) Demo Day on April 16, 2026 at the UC Berkeley Richmond Field Station.
“I love hosting demo day, it’s an amazing opportunity to show people the cutting-edge research that we, our partners, and friends are working on,” says ITS Berkeley Acting Director Scott Moura. “The talent and technology on display is inspiring.”
DRIVE AI is a new initiative from the Institute of Transportation Studies at UC Berkeley that brings industry, government, and academia partners in the mobility ecosystem together to incorporate artificial intelligence into data, infrastructure, vehicles, electrification, systems, and workforce needs. Based at the Richmond FIeld Station, the DRIVE AI consortium members can use the AV test track, V2X corridors, drone testing zones, charging and microgrid infrastructure, and open-air labs while engaging with faculty and students.
Participants at Demo Day included:
- ITS Berkeley/UC Berkeley Civil and Environmental Engineering Drones: Shared prototypes and demonstrated autonomous drones that will deliver critical medical supplies in rural areas.
- ITS Berkeley/Partners for Advanced Transportation Technology Workzone Safety: Showed connected sensor technology, including C-V2X enabled RSUs and “smart barrels," combined with digital twins and the ability to provide real-time workzone information compatible with the WzDX standard. These create safer construction zones for all roadway users and especially for the road crews, such as by giving advanced warning of dangerous driving upstream. They also shared the related project for connectivity between autonomous vehicles and emergency vehicles; see below for more information in the Safe Streets for All grant.
- ITS Berkeley/ Partners for Advanced Transportation Technology Digital Infrastructure and Simulator: Showed video and simulator technology using digital twin software to replicate and demonstrate street and intersection safety. This includes the blending of virtual and physical environments, such as via hardware-in-the-loop simulation, so that applications can be safety tested in both simulation and at RFS before transitioning to public roads.
- Simforge: Using a variety of videos, showed how to use scalable digital twin information in simulation-ready urban environments.
- Roundabout Technologies: Through video and a digital library, showed AI-powered traffic control systems that make intersections safer by using computer vision and commodity traffic cameras to build a 3D map of everything happening at the intersection in real time.
- Autolane: Demonstrated how autonomous vehicles pull into dedicated curb space for seamless order pick up, using a robot to put orders in the car, and talked about how they deliver goods and coordinate rides.
- Robot.com: Demonstrated the kiwibot, which is used to deliver items and meals, and talked about newer robotic platforms to move cargo, carry ads, and inspect infrastructure.
- Upshift: Talked about building their a personal autonomous vehicle subscription car service and the platform behind it that offers hybrid and electric cars for delivery.
- FloatCargo: Showed their first prototype of an airship for freight and goods movement at a massive scale with minimal cost.
- Wild: Demonstrated a trailer bed that pulls its own weight and provides power with their electric powertrain kit can integrate directly into an existing trailer.
- Safe Streets 4 All Grant: Advanced Mobility Group and Contra Costa County Transportation Authority: Showed their shared autonomous vehicle (SAVs) and the technology behind it that is improving mobility and safety, focusing on areas near schools and transit.
- CalSol, UC Berkeley Solar Car Racing team: Undergraduate students from the team showed and talked about the design, build, test, and race process of their fully solar-powered vehicle.