Outsight joins forces with Pôle Véhicule du Futur

Frequently Asked Questions

• What is Pôle Véhicule du Futur and what does it do?

  Pôle Véhicule du Futur is a French competitiveness cluster founded in 2005, based across the Burgundy-Franche-Comté and Grand Est regions. It connects over 500 members, including automotive manufacturers, research institutions, and local authorities, to accelerate innovation in sustainable mobility and advanced vehicle technologies. Competitiveness clusters in France receive co-funding from the French government and regional bodies, giving member companies access to shared R&D programs and pilot projects that would be cost-prohibitive to run independently. Outsight, whose infrastructure-based Physical AI platform is already deployed in automotive manufacturing environments such as BMW facilities, joined the cluster to contribute LiDAR perception and Motional Digital Twin expertise to the broader mobility innovation ecosystem.

• What is Roadside Autonomous Driving and how does it differ from onboard autonomous vehicle perception?

  Roadside Autonomous Driving (RAD) places the perception stack in fixed infrastructure rather than aboard the vehicle. Sensors mounted on poles or gantries cover an entire parking area or intersection and guide vehicles through the space externally, removing the need for a full onboard sensor suite on each car. This infrastructure-based approach is a specific segment of the broader Automated Valet Parking market and is directly complementary to onboard autonomous driving systems, which only see the road from the vehicle's own vantage point. Outsight's Infrastructure-based Physical AI model follows the same principle: LiDAR sensors deployed in the environment, rather than on moving entities, build a real-time Motional Digital Twin of every vehicle and person moving through a site, a capability already applied in BMW and other automotive manufacturing environments.

• How does infrastructure-based LiDAR support autonomous vehicle development beyond the vehicle itself?

  Infrastructure-based LiDAR sensors provide a site-wide, real-time 3D picture that moving vehicles cannot generate on their own, because each vehicle only perceives its immediate surroundings. Fixed roadside sensors share that broader view with every vehicle in the zone, enabling coordinated maneuvers, early hazard warnings, and flow optimization across the full site. Outsight's approach, which it calls Infrastructure-based Physical AI, places LiDAR in the infrastructure rather than on the vehicles themselves, feeding a real-time Motional Digital Twin of every person, vehicle, and robot moving through a site. This shared perception layer reduces the sensor and compute requirements on individual vehicles and is particularly relevant in constrained environments such as parking structures, logistics yards, and controlled intersections.

• Why do smart-mobility clusters focus on real-time data processing rather than post-trip analysis?

  Transportation systems require decisions in seconds, not hours. A parking guidance system that updates occupancy every few minutes gives drivers inaccurate routing, and an intersection safety system that detects a pedestrian conflict after the fact offers no protective value. Real-time processing, where sensor data is converted to actionable spatial intelligence in under 50 milliseconds, is the architectural requirement for use cases such as dynamic lane control, autonomous parking guidance, and live congestion response. Outsight's SHIFT platform is built around exactly this constraint, running a sub-50ms end-to-end pipeline that feeds a real-time Motional Digital Twin of how people and vehicles move through a site. Batch analytics serves planning and reporting; real-time processing serves operations.

• Can the same spatial intelligence platform handle both people flow in transport hubs and vehicle tracking in ITS applications?

  Yes, because both use cases share the same underlying pipeline: 3D LiDAR sensors detect objects, a perception layer classifies them by shape and motion (pedestrian, car, bus, two-wheeler), and an analytics layer surfaces KPIs against operator-defined zones. The classification models differ between a crowded terminal and a roadway intersection, but the sensor network architecture, the tracking logic, and the data delivery format are consistent across both domains. Outsight's SHIFT platform is built on exactly this principle, serving airport terminals such as Dallas Fort Worth and Paris-Charles de Gaulle alongside smart-city intersections like those in Bellevue and train stations operated by SNCF, all without a separate codebase for each environment.

• How does joining a competitiveness cluster like Pôle Véhicule du Futur benefit a deep-tech software company?

  Competitiveness clusters in France provide structured access to automotive OEMs, Tier-1 suppliers, and public research labs through a single membership, reducing the business development overhead of reaching each organization separately. For a company like Outsight, whose SHIFT platform delivers real-time 3D spatial intelligence for vehicles, infrastructure, and factories, the immediate value is pilot project opportunities with manufacturers already operating in adjacent domains such as connected vehicles and energy-efficient propulsion, where anonymous motion data can validate system performance. Clusters also facilitate access to European and French national R&D funding programs, which are often only open to consortium applicants rather than solo companies, making membership strategically important for deep-tech firms pursuing large-scale deployments across automotive and mobility environments.