Spaces:
Paused
Paused
| 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] | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| ``` |