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SOTIF scenario annotation: Triggering conditions and functional deficiencies tagging for ISO 21448

SOTIF scenario annotation: Triggering conditions and functional deficiencies tagging for ISO 21448

Many real-world incidents occur even when all system components are functioning as intended. The problem lies in the system’s limitations in perceiving or responding correctly to complex environmental conditions. To address these, the automotive industry introduced ISO 21448, known as safety of intended functionality (SOTIF). SOTIF addresses the

Cross-Fleet Sensor Consistency Annotation: Validating Perception Across Vehicle Variants and Sensor Generations

Cross-Fleet Sensor Consistency Annotation: Validating Perception Across Vehicle Variants and Sensor Generations

Validating HD Map Localization Accuracy for AVs

Validating HD Map Localization Accuracy for AVs

Vehicle-based DVS annotation

Vehicle-based DVS annotation

ECU Timing and Latency Annotation: Validating Real-Time Perception Pipelines for Autonomous Vehicles

ECU Timing and Latency Annotation: Validating Real-Time Perception Pipelines for Autonomous Vehicles

Annotating Gated Camera Data for Automotive Night AI

Annotating Gated Camera Data for Automotive Night AI

Top 5 data annotation providers powering physical AI and embodied systems

Top 5 data annotation providers powering physical AI and embodied systems

Data annotation

SOTIF scenario annotation: Triggering conditions and functional deficiencies tagging for ISO 21448

SOTIF scenario annotation: Triggering conditions and functional deficiencies tagging for ISO 21448

Many real-world incidents occur even when all system components are functioning as intended. The problem lies in the system’s limitations in perceiving or responding correctly to complex environmental conditions. To address these, the automotive industry introduced ISO 21448, known as safety of intended functionality (SOTIF). SOTIF addresses the

Cross-Fleet Sensor Consistency Annotation: Validating Perception Across Vehicle Variants and Sensor Generations

Cross-Fleet Sensor Consistency Annotation: Validating Perception Across Vehicle Variants and Sensor Generations

Vehicle-based DVS annotation

Vehicle-based DVS annotation

ECU Timing and Latency Annotation: Validating Real-Time Perception Pipelines for Autonomous Vehicles

ECU Timing and Latency Annotation: Validating Real-Time Perception Pipelines for Autonomous Vehicles

Annotating Gated Camera Data for Automotive Night AI

Annotating Gated Camera Data for Automotive Night AI

Top 5 data annotation providers powering physical AI and embodied systems

Top 5 data annotation providers powering physical AI and embodied systems

Sensor soiling and obstruction annotation

Sensor soiling and obstruction annotation

AI news

Validating HD Map Localization Accuracy for AVs

Validating HD Map Localization Accuracy for AVs

Around the development of autonomous transport, there is a widespread opinion that a self-driving car navigates in space exclusively by reading the road situation with cameras and sensors in real-time. In reality, the safe movement of robocars is almost impossible without the use of HD maps, which play

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