--- title: "Automotive AI Data: Voice, Perception and Evaluation | YPAI" url: https://ypai.ai/automotive/ description: "Automotive AI data in one delivery: in-cabin voice, camera, LiDAR and sensor-fusion annotation, and evaluation for OEM, Tier-1 and fleet teams." source: "src/copy/routes (route /automotive/)" --- # Automotive AI data recorded where the car is: the cabin and the road. > Automotive AI data in one delivery: in-cabin voice, camera, LiDAR and sensor-fusion annotation, and evaluation for OEM, Tier-1 and fleet teams. Automotive AI data recorded where the car is: YPAI scopes in-cabin voice, perception data, and evaluation under one delivery. Native-speaker speech. Camera, LiDAR and sensor-fusion annotation. Acceptance criteria written before production. Norwegian legal entity · EEA residency by default · DPA per engagement Programme · motorway · mixed sensors - Languages available through the YPAI contributor and specialist network - 3D point-cloud and sensor-fusion annotation for ADAS and autonomy programmes - Speaker recruitment and recording in the required language markets - Processing and residency options scoped per engagement, with a DPA Evaluation, collection and annotation work across OEM and Tier-1 programmes. Relationship and scope vary by engagement. ## The benchmark is a quiet room. The model meets the road. YPAI scopes the people, vehicles, sensors and conditions that decide whether automotive AI works outside the lab. Engine load, weather, cabin acoustics and overlapping speech shape the signal. and local entities shape the language. determine whether voice or perception data can ship. ## One automotive engagement, several data pathways. Start with in-cabin voice, perception annotation, or evaluation. The same delivery can carry supporting modalities when the brief requires it. ### In-cabin voice and speech Wake-word, intent, multilingual commands and cabin-noise data. Native speakers in the required markets. ### Voice, perception and evaluation in one delivery Scope the programme around the actual cabin, sensor suite and acceptance criteria, then attach the evidence trail the buyer needs to review. ### ADAS and autonomy annotation Image, video and 3D labelling for driving scenarios, objects and behaviours, against the project taxonomy. ### LiDAR and sensor fusion Point-cloud, cuboid and time-synchronised multi-sensor annotation when the brief extends into the perception stack. ### Evaluation and controlled delivery Review, gap collection and accepted batches. Implementation support stays scoped to the data and evaluation work. Market, sensor suite, rights and acceptance requirements turn this scope into ## One automotive data project, three practical starting points Three ways to enter the same controlled delivery. ### Build a new voice or perception corpus - For a new market, cabin profile, sensor suite or vehicle experience. - Turn scenarios, speaker or sensor requirements, and output specs into a capture and annotation plan, then deliver an accepted corpus with the agreed metadata and labels. ### Evaluate and remediate an existing system or dataset - For a system or dataset with known failure conditions. - Use model outputs and failure cases to isolate gaps by market, cabin condition or operating domain, then define the right evaluation, review, re-annotation or targeted collection. ### Run controlled production through accepted delivery - For a defined production brief, ready to execute. - Turn the specification, rights model and acceptance criteria into qualification, calibration, production, QA, review, rework and accepted batches. ## Mobilisation follows the specification. The delivery plan takes shape around the actual work. ### Qualification and calibration - Align participant or asset profiles, technical setup, review criteria and capture protocol before production begins. ### First accepted wave - Prove the specification against submitted material, then use the accepted batch to set the working forecast. ### Rolling delivery - Capture, review, rework and replacement coverage move together against the programme brief. The forecast uses the unit that matters to the programme: accepted speaker-hours, recordings, frames, sessions or assets. ## Training data you can stand behind. Managed collection, annotation, validation and evaluation. A managed data operation, accountable for every accepted batch. Road, weather, cabin acoustics and sensor conditions change what must be collected and how it is judged. - Drivers, passengers, road surface, weather and sensor suite. - Intent, objects, tracks and agreed taxonomies. - Noise, cabin character, lighting and domain variation. - Does the experience still hold when driving? ## One record runs the whole project. Reviewers and operations teams see the same task status and version history. ### Qualification ### Planning ### Collection ### Verification ### Native review ### Acceptance ### Delivery Failed material returns for recapture, rework, replacement or rejection according to the project rules. Accepted material moves into versioned batches, manifests and secure delivery. Role-based dashboard and client-portal access can be provided for YPAI-managed work. YPAI-managed infrastructure, customer systems or a hybrid API access and custom integrations are available on a You can keep your own tools ## Scope an automotive AI project. Bring the programme type, primary use case, and any regulatory or homologation deadline. A named project lead replies within one EU business day with a feasibility read. - In-cabin voice + ADAS perception under one master DPA - EEA residency by default, Norwegian Aksjeselskap - GDPR Article 28 DPA included with every engagement - OEM, Tier-1, aftermarket and fleet programmes GDPR Article 28 · EU AI Act Article 10 · EEA jurisdiction Support for automotive AI development, from data collection through deployment. ## Autonomous vehicle technologies ### [Autonomous vehicle validation and testing](https://ypai.ai/data-collection/) - Validation and testing against the driving scenarios agreed for the programme ### [LiDAR annotation and sensor fusion](https://ypai.ai/annotation/lidar-3d-point-cloud-annotation-services/) ### [Autonomous vehicle annotation services](https://ypai.ai/ai-data-annotation/) - Labelling of driving scenarios, objects and behaviours for ML training ### [LiDAR and 3D point cloud annotation](https://ypai.ai/annotation/lidar-3d-point-cloud-annotation-services/) - 3D data labelling for depth perception and spatial understanding ### [Sensor fusion annotation services](https://ypai.ai/annotation/sensor-fusion-annotation-services/) - Synchronised multi-sensor data annotation for environmental perception ## Voice and user experience ### [Speech data for automotive voice recognition](https://ypai.ai/voice-recognition-for-automotive/) - In-car speech collected, annotated and evaluated across languages, dialects and cabin acoustics ### [In-cabin voice, wake-word and driver-monitoring audio](https://ypai.ai/solutions/automotive/) - Wake-word, intent and DMS audio recorded in real cabin noise, with the supporting perception data ## Data infrastructure and services ### [Video annotation services](https://ypai.ai/annotation/video-annotation-services/) - Frame-by-frame labelling for dashcam analysis, parking assistance and driver monitoring ### [Image annotation](https://ypai.ai/annotation/image-annotation/) - Labelling for object detection, lane recognition and traffic-sign classification ### [Data collection](https://ypai.ai/data-collection/) - Real-world driving data capture across conditions, edge cases and scenarios