Here is the structured skill file derived from the provided ontology and taxonomy. This format is designed for ingestion by an autonomous AI agent, perception pipeline, or simulation framework. --- # **Skill Definition: Autonomous Driving Semantic Ontology & Taxonomy** ## **Domain Overview** * This skill provides a machine-readable vocabulary to describe the driving environment for Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS). * It transitions validation from mileage accumulation to scenario-based testing. ## **1. Supported Standards & Frameworks** * **ISO 34501**: Defines the core vocabulary for test scenarios, distinguishing between static scenes and temporal scenarios. * **ISO 34502**: Establishes a scenario-based safety evaluation framework based on scenario criticality. * **ISO 34503**: Specifies the Operational Design Domain (ODD), detailing environmental, geometric, and geographic conditions. * **ISO 21448 (SOTIF)**: Identifies rare triggering conditions and functional insufficiencies for the Safety of the Intended Functionality. * **ASAM OpenX Suite**: Utilizes OpenDRIVE for static road networks, OpenSCENARIO for dynamic behaviors, OpenLABEL for multi-sensor data annotation, and OpenXOntology for semantic relationships. ## **2. Entity Classification Taxonomy** ### **A. Road Users & Kinematic Profiles** * **Passenger Vehicles**: Passenger cars act as the base model for gap acceptance. SUVs create occlusion zones for forward-facing cameras. Vans have minimal rear visibility. Pickup trucks have variable mass based on payloads. Taxis execute highly unpredictable route behaviors. * **Heavy & Commercial Vehicles**: Buses block entire lanes during stops. School buses trigger strict longitudinal stopping rules. Heavy trucks require avoidance in lateral blind spots. Semi-trailers mandate extended lateral safety buffers due to trailer sweep. * **Emergency Vehicles**: Police vehicles, fire trucks, and ambulances possess extreme acceleration or mass and mandate immediate yielding or multi-agent negotiation. * **Vulnerable Road Users (VRUs)**: Pedestrians require continuous pose estimation. Children pose a high risk of occlusion behind parked cars. Wheelchair users require robust LiDAR detection due to atypical radar signatures. Skaters have highly unpredictable trajectory envelopes. * **Micro-Mobility**: Cyclists require recognition of hand signals for turning. E-bikes feature the visual signature of a bicycle but the acceleration of a scooter. * **Animals**: Domestic animals execute rapid startle responses. Wildlife herd behavior implies that detecting one animal strongly predicts others. Livestock can completely obstruct roadways. ### **B. Static Road Objects (The Scenery)** * **Drivable Surfaces**: Includes roads, lanes, shoulders (for minimal risk maneuvers), bike lanes, bus lanes, intersections, roundabouts, highway ramps, and parking spaces. * **Physical Constraints & Barriers**: Curbs act as hard boundaries for path planners. Sidewalks are the primary domain of VRUs. Guardrails are high-consequence collision objects. Concrete barriers create multipath radar interference. * **Vertical Infrastructure**: Utility poles serve as reliable localization landmarks. Street lights can cause localized glare or sensor oversaturation. * **Pavement Markings**: Degradation of lane markings (e.g., from snow) leads to functional insufficiencies. Stop lines trigger mandatory longitudinal deceleration algorithms. ### **C. Dynamic & Transitory Objects** * **Uncontrolled Hazards**: Falling objects or debris necessitate rapid free-space recalculation. * **Temporary Infrastructure**: Construction equipment overrides standard HD maps and forces reliance on real-time sensor fusion. * **Automated Agents**: Delivery robots lack standard human communication cues. Drones confound standard 2D bird's-eye-view perception projections. ## **3. Activities and Semantic Maneuvers** * **Vehicle Activities**: Rapid braking from a lead vehicle triggers emergency deceleration protocols. Open doors instantly redefine drivable free space. * **Pedestrian Activities**: Walking speed determines the predicted intersection point with the ego vehicle's path. Jaywalking triggers immediate emergency deceleration. * **Traffic Control Elements**: Regulatory signs (e.g., Stop, Yield) enforce mandatory constraints. Variable message signs (VMS) require complex natural language processing (NLP) to interpret. Flaggers and police gestures supercede all static lights and signs. ## **4. Operational Design Domain (ODD) Context** * **Weather & Visibility**: Rain and snow distort camera lenses and obscure lane markings. Fog and smoke scatter LiDAR pulses, forcing heavier reliance on radar. * **Surface & Force**: Ice reduces tire-road friction, expanding required stopping distances. High crosswinds require compensatory steering torque. * **Lighting**: Dawn and dusk create severe horizontal solar glare, which is a classic SOTIF triggering condition. ## **5. Semantic Ontology Graph** * The system utilizes ontological mapping to reason about physical and legal dependencies. * **Vehicle -> is_cutting_into -> Lane**: Triggers deceleration to restore safety margins. * **Pedestrian -> intends_to_cross -> Crosswalk**: Mandates yielding the right-of-way. * **Weather (Ice) -> degrades -> Road (Surface)**: Updates global friction coefficient variables. * **Police Officer -> overrides -> Traffic Light**: Forces the system to ignore the traffic light state. --- ## **6. Machine-Readable Integration Schema (ASAM OpenLABEL)** The following JSON schema can be directly ingested into your project's perception pipeline to model SOTIF corner cases, such as a pedestrian crossing in heavy rain. ```json { "openlabel": { "metadata": { "schema_version": "1.0.0", "scenario_name": "Urban_Pedestrian_Crossing_Rain_SOTIF", "description": "Ego vehicle encounters a jaywalking pedestrian in heavy rain, triggering an RSS braking maneuver.", "ontologies": { "asam_openx": "https://www.asam.net/standards/asam-openxontology" } }, "contexts": { "env_001": { "name": "Heavy_Rain", "type": "EnvironmentalCondition", "attributes": { "precipitation_rate": "15.0 mm/hr", "visibility_reduction": true, "surface_friction_coefficient": 0.5 } }, "odd_001": { "name": "Urban_Arterial", "type": "OperationalDesignDomain", "attributes": { "speed_limit": "50 km/h", "road_type": "CityStreet", "region": "US_RightHandDrive" } } }, "objects": { "ego_vehicle": { "name": "Ego", "type": "Vehicle", "sub_type": "PassengerCar", "kinematics": { "velocity_x": 12.5, "acceleration_x": -4.0 } }, "ped_001": { "name": "Pedestrian_Adult", "type": "VulnerableRoadUser", "sub_type": "Pedestrian", "attributes": { "pose": "Walking", "is_occluded": false } }, "infra_001": { "name": "Main_Crosswalk", "type": "StaticRoadObject", "sub_type": "Crosswalk" } }, "actions": { "act_001": { "type": "Emergency_Braking", "actor": "ego_vehicle", "trigger": "rss_longitudinal_distance_violation" }, "act_002": { "type": "Jaywalking", "actor": "ped_001", "description": "Pedestrian crossing outside designated crosswalk infra_001." } }, "relations": { "rel_001": { "type": "interacts_with", "subject": "ego_vehicle", "object": "ped_001" }, "rel_002": { "type": "modifies", "subject": "env_001", "object": "ego_vehicle", "description": "Rain reduces optimal braking friction, increasing required RSS stopping distance." } }, "frames": { "0": { "timestamp": 1690384512.001, "frame_properties": { "sotif_triggering_condition": "High_Rain_Glare" }, "object_data": { "ped_001": { "bbox_3d": [15.2, 3.1, 0.0, 0.6, 0.6, 1.7, 1.57] } } } } } } ```