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Sensor Timestamping in Multi-ECU Systems: Where PTP and gPTP Break Down

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A camera frame captured at the front of the vehicle, a LiDAR scan rotating at 10 Hz, and an IMU running at 200 Hz all generate data that the ADAS fusion stack needs to align in time before it can produce a coherent world model. If these sensors are controlled by different ECUs running different local clocks, and those clocks are not synchronized to a common timebase with sub-millisecond accuracy, the fusion algorithm is working with data that describes different moments in time as though they were simultaneous. At 100 km/h, a one-millisecond timestamp error corresponds to approximately 28 mm of vehicle displacement — enough to introduce meaningful localization uncertainty in a system trying to track objects at centimeter precision. This is the engineering motivation behind deploying Precision Time Protocol or its automotive-profile variant, Generalized Precision Time Protocol, across multi-ECU vehicle networks. Both protocols solve the distributed clock synchronization problem well und

A camera frame captured at the front of the vehicle, a LiDAR scan rotating at 10 Hz, and an IMU running at 200 Hz all generate data that the ADAS fusion stack needs to align in time before it can produce a coherent world model. If these sensors are controlled by different ECUs running different local clocks, and those clocks are not synchronized to a common timebase with sub-millisecond accuracy, the fusion algorithm is working with data that describes different moments in time as though they were simultaneous. At 100 km/h, a one-millisecond timestamp error corresponds to approximately 28 mm…

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