In addition to computational modeling and simulation, the lab is equipped with several robots capable of teleoperation and autonomous motion. Mobile robots with onboard processors are augmented with sensors and wireless communication equipment to operate independently and in coordinated assemblies.

The one-tenth autonomous car is a small-scale platform for Connected Autonomous Vehicle (CAV) research that outperforms currently available commercial options in several essential benchmarks. The platform is built around a radio-control (RC) car using high-performance brushless DC motors, allowing the vehicle to reach a maximum speed of 70 mph, expanding the possibilities for higher-speed research applications. Furthermore, this platform has a robust sensor suite. It features a state-of-the-art embedded GPU unit for onboard computation, allowing real-time control over various challenging operations. Lane-keeping as an Advanced Driving Assistance System (ADAS) function was implemented and evaluated using the platform for demonstration and comparison. It has also been used for teleoperation research in our group. Other commercially available mobile robots are expensive, offer limited capabilities, are more complicated to modify for various research needs, are more difficult to interface with other robots (cars), and could have proprietary software/hardware features that render them less flexible and less adaptable to specific research needs. The developed 1/10^th car overcomes these limitations and provides a highly flexible, cost-effective alternative for automated/autonomous and connected vehicle research and development projects.

You can learn more about the project in this published paper.

A small emulated track space is created in an indoor lab to test mobility functions in a communication-enabled environment. Continuous tracks, intersections, building blocks, and road features are simulated in a scaled-down model space where the mobile robots can be tested. Road markings for lanes and scaled-size traffic devices (stop signs, etc.) are installed for the robot’s (vehicle’s) vision processing. Although the robots are equipped with outdoor GPS, an indoor GPS function is required for localization. An overhead camera and digitization of the track map provide a GPS-like function. Proximity sensors emulate traffic loop detectors to determine the presence or absence of robots in intersections and estimate their speeds. Robots communicate with each other and with a central station if required.

Several other features are added to enable a complete scaled-down testing environment. The robot’s vision system can maintain lane keeping or road following using trajectory plans. Robots can be tested for teleoperation and autonomous driving using vision, GPS, and other sensors. Various functions such as lane-changing maneuvers, collision avoidance, car-following, platooning, intersection control, and numerous other connected vehicle functions can be tested in this environment. We are building and enhancing this environment as project needs arise.

Full Cabin Driving Simulator

A full-cabin driving simulator (developed by Realtime Technologies) simulates driver-controlled and autonomous driving in an intelligent traffic environment with several other advanced simulation capabilities. The simulator offers an embedded full-cabin environment with realistic control features; full vehicle dynamics with adjustable parameters; curved projection with a 180-degree driver field of view; day, night, rain, snow, and fog driving environments; an extensive library of roads, infrastructure, and traffic control devices; along with advanced data acquisition systems. A unique feature is the ability of multiple drivers to drive within the same scenario from different networked simulators.

Desktop Simulator (Networked)

A desktop simulator using the same software is linked/networked with the full cabin simulator to allow simultaneous driver-in-the-loop or autonomous driving on the same road/infrastructure scenarios. Multiple drivers driving within the same scenario is also possible, allowing extensive testing of ADAS in a safe environment.

The lab has human (driver) monitoring systems such as eye-monitoring and brain EEG sensors for various perception-response, controls, and ADAS experimentation. In collaboration with other departmental and College of Engineering labs, we have an extensive and comprehensive set of physiological monitoring systems.

An autonomous vehicle was developed based on Dr. Eskandarian's previous lab’s vehicle at Virginia Tech. The car has been updated with new sensors and up-to-date adaptable capabilities to accommodate experimentation of various sensory platforms, sensor fusion methods, decision algorithms, and control systems for driverless and connected vehicle research and testing.

This indoor drone system consists of nano drones that can be flown individually or as a swarm, carrying sensors for guidance, navigation, and control and video cameras with AI computer vision capability. Nano drones with AI capability can fuse multiple sensing and cooperate through communications on their trajectories. They can also interact with robotic cars through wireless communication channels for cooperative perception and control. This collaboration can assist in autonomous driving and remote control of ground robots and provide advanced driver assistance to humans.

The Lab includes a stereo indoor positioning system for both aerial and ground vehicles that can emulate decentralized positioning, resembling GPS, or a centralized positioning system with bidirectional communications, where an elevated device could serve as a standard reference for information sharing and coordination. For realistic testing, the lab floor is prepared with a homogeneous surface where traffic patterns, including street sections, roundabouts, and intersections, are displayed realistically.