Automated Valet Parking: Roadside Autonomous Driving Explained

Frequently Asked Questions

• What is the difference between Roadside Autonomous Driving and standard Automated Valet Parking?

  Standard Automated Valet Parking relies on sensors and compute mounted aboard each individual vehicle, so the car must be equipped for autonomy before it can navigate. Roadside Autonomous Driving flips that model: the intelligence sits in fixed infrastructure sensors, and any production vehicle, including models with no onboard perception hardware at all, can be guided to a parking space. This infrastructure-based approach is exactly the model Outsight applies through its Motional Digital Twin, where LiDAR sensors deployed in the environment track every vehicle and person in real time without requiring modifications to the vehicles themselves. The facility pays once for the infrastructure rather than equipping every vehicle, which is why the approach is economically attractive in high-volume industrial settings such as automotive manufacturing plants.

• Does a car need to be an autonomous vehicle to work with a RAD parking system?

  No. Infrastructure-based RAD systems require only that the vehicle can accept steering, acceleration, and braking commands over a standard interface. The perception, localization, and path planning all run on fixed LiDAR sensors and edge compute installed in the facility, not on hardware aboard the car. Outsight's approach to this problem, built on its Motional Digital Twin, places all 3D sensing and processing in the infrastructure, tracking every vehicle in real time without requiring any onboard modification. This means conventional production vehicles rolling off an assembly line can be marshalled autonomously as-is, which is the key commercial advantage over traditional AVP approaches that depend on sensor-equipped or purpose-built vehicles.

• How does an infrastructure LiDAR system communicate position data to a moving vehicle in real time?

  Fixed LiDAR sensors mounted in the facility continuously track the vehicle's precise 3D position and orientation. That data is processed by Spatial AI software and streamed to the vehicle's control interface in real time, at sub-50ms end-to-end latency. Outsight's SHIFT platform exemplifies this architecture: infrastructure-based 3D LiDAR sensors feed a real-time Motional Digital Twin, which publishes position and trajectory data to a vehicle's drive-by-wire interface fast enough to close the control loop without any onboard perception. The vehicle's drive-by-wire system translates the incoming commands into steering, throttle, and braking adjustments. The approach mirrors how air traffic control guides aircraft: ground-based awareness feeding commands to a moving object.

• What obstacles besides other cars does a RAD system need to detect inside a factory lot?

  Factory and warehouse lots introduce a more varied obstacle set than public parking structures. Fixed infrastructure LiDAR must reliably classify pedestrians, forklift trucks, loading equipment, pallet jacks, and transient objects such as cones and barriers, all under varying indoor lighting and alongside reflective vehicle surfaces. The same 3D point-cloud pipeline that tracks vehicle position classifies co-located entities by shape and motion, assigning each a separate tracked ID so the path-planning layer can compute collision-free routes dynamically as the scene changes. Outsight addresses exactly this environment through its Motional Digital Twin, which runs a sub-50ms end-to-end perception pipeline across mixed human, vehicle, and robot traffic on factory floors, as demonstrated in deployments at BMW and other automotive manufacturers.

• How does LiDAR handle the highly reflective surfaces of new cars in a factory parking environment?

  LiDAR pulses respond to the geometry of a surface rather than its colour or reflectivity in the visible spectrum. Highly polished bodywork does scatter near-infrared returns unevenly, but modern multi-beam LiDAR units emit millions of pulses per second across many vertical channels, so even partial returns from curved or reflective panels are enough to reconstruct a full bounding box around the vehicle. Software-side filtering further rejects specular noise while preserving the structural point returns that define vehicle shape and orientation. Outsight addresses this challenge directly in factory environments, with infrastructure-based LiDAR deployments at BMW and other automotive manufacturers using the SHIFT platform to track vehicles and assets in real time, compensating for reflective surfaces through multi-return processing and sensor fusion across compatible hardware from vendors including Hesai, RoboSense, and Ouster.

• What is the AVP market projected to be worth by 2031?

  The Automated Valet Parking market was valued at approximately USD 1.02 billion in 2023 and is projected to reach around USD 44.25 billion by 2031, representing roughly a 44-fold increase over eight years. That growth rate reflects rising demand from both automotive manufacturing, where RAD systems reduce manual vehicle handling costs, and commercial facilities seeking to maximize space utilization and reduce dwell time in parking structures. Infrastructure-based perception is central to enabling this shift: Outsight's Motional Digital Twin tracks vehicles and robots in real time through fixed LiDAR sensors, an approach already deployed in BMW and other automotive manufacturing environments where precise, anonymous vehicle localization is operationally critical.