Research Facilities

6GCAMLab

R&D at Connected and Automated mobility, leveraging V2X, advanced positioning and computer vision tech

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6GCAMLab is part of the global transformation of the automotive industry towards a model of cooperative, connected, and autonomous mobility (CCAM).

Its goal is to improve road safety and reduce environmental impact through the use of advanced mobile networks and smart sensors. The laboratory integrates V2X solutions, environment perception systems, and AI tools for mobility prediction, establishing itself as a key environment for validating the mobility of the future.

Featured Equipment

  • Measurement and instrumentation equipment for V2X experimentation, including a prefabricated outdoor laboratory and indoor instrumentation for C-V2X and ITS-G5 testing under controlled conditions.
  • Hardware development tools, such as programming and debugging probes, FDM and resin 3D printers, and rapid manufacturing systems.
  • Electronic and laboratory instrumentation: multimeters, oscilloscopes, spectrum analyzers, microscopes, and soldering stations.
  • Private 5G network for validating advanced communications in experimental environments.
  • Autonomous and connected vehicles for the development and validation of smart mobility solutions.
  • ROS-based autonomous robotic platform for automated logistics transport applications.
  • Technological solutions aimed at vulnerable users in connected mobility environments.
  • Advanced computing and communication platforms, including automotive chips with AI capabilities, 5G modules, SoM, SBC boards, and IoT devices.
  • Network and computing equipment, such as edge computing nodes, PoE switches, and servers with GPUs for intensive data processing.
  • Teleoperation control room for real-time monitoring, remote control, and vehicle management.
  • V2X ecosystem elements, such as RSU and OBU units for communication between vehicles, infrastructure, and road users.
  • Advanced positioning infrastructure based on GNSS-RTK technologies, including base stations, receivers, and correction transmission systems.
  • Complementary localization technologies, such as inertial systems and UWB.
  • Computer vision infrastructure, including LiDAR sensors and RGB, stereo, and thermal cameras for multimodal perception.
  • Calibration patterns for adjusting and validating artificial vision systems.
  • Environmental sensors, including presence, noise, and air quality sensors, as well as ultrasonic, infrared, and LiDAR sensors.
  • Advanced computing capabilities, including rack servers, embedded AI units, GPU-equipped hardware, and compact edge servers.
  • Auxiliary equipment, such as PTZ cameras, tripods, 3D scanning systems, and 360º immersive cameras for virtual reality applications.
  • Cybersecurity infrastructure based on edge and cloud servers with Trusted Execution Environments (TEE) and security modules (TPM).
  • Anti-hacking devices and servers for protecting embedded systems and V2X infrastructures.
  • Secure private 5G network, including small cells and core, along with client devices for validating protected communications.

Key Technologies

  • Autonomous and Connected Mobility through V2X and 5G/6G networks.
  • Edge Computing and Artificial Intelligence for intensive data processing.
  • Embedded systems, SBC platforms, and architectures for real-time connectivity.
  • Advanced perception and artificial vision (LiDAR, thermal cameras, and 3D scanning).
  • High-precision positioning and inertial navigation (INS/UWB).
  • Cybersecurity in distributed systems and protection of critical infrastructures.

Use Cases

  • Advanced safety for vulnerable users: This use case focuses on protecting pedestrians and cyclists in complex traffic scenarios, with both connected and non-connected vehicles. Vulnerable users transmit their position in real time using V2X technologies and high-precision positioning systems. Advanced sensors and energy-efficient equipment are used to ensure their location is correctly detected. The goal is to reduce risks at intersections, crosswalks, and bike lanes, even when visibility is limited.
  • Cooperative driving: This use case explores the coordination between connected autonomous vehicles in complex traffic scenarios, such as intersections, lane entries, and overtaking. Vehicles exchange information about planned trajectories and positions via V2X networks and edge computing systems. The shared information allows for the cooperative adjustment of speeds, priorities, and maneuvers to optimize traffic flow. Multiple perception, communication, and high-precision positioning technologies are used to enable real-time coordination.
  • Teleoperation of autonomous vehicles: This use case focuses on teleoperated driving, where an autonomous vehicle receives remote assistance for situations it cannot manage on its own. An operator in a control room performs indirect or direct control maneuvers for the necessary time or distance. Service performance and the quality of remote operation are evaluated by collecting key indicators. The scenario ranges from confined environments to public roads and cross-border mobility.