Computer Vision Inference Architecture for Multi-Sensor Fusion Systems

Overview of Technical Issues:

In multi-sensor fusion systems, the data fusion module insufficiently synchronizes inputs from sensors operating at different sampling rates, causing temporal misalignment that corrupts fused data quality and produces unreliable inference results; the goal is to achieve robust real-time inference with consistent multi-sensor integration under varying operational loads.

Solution directions generated for this problem

Problem Direction 1 :

ImproveSynchronization timing precision
VS
ConstraintProcessing latency

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Method and apparatus for hierarchical data unit based video encoding and decoding including quantization parameter prediction
Innovative Solution Refine solution

Hardware-level timestamp pre-alignment at sensor acquisition stage

Pre-align sensor data at acquisition stage using hardware timestamping
How to solve :
  • Embed GPS-disciplined clock modules (±1μs accuracy) at each sensor node to tag data at capture moment with absolute timestamps
  • Deploy FPGA-based pre-alignment units at sensor edge that interpolate and resample 10-100Hz streams to unified 30Hz grid using pre-computed Lagrange coefficients, completing alignment in <5ms hardware latency
  • Transmit pre-synchronized 30Hz data streams to fusion module via time-triggered Ethernet (IEEE 802.1AS) with deterministic <2ms jitter, eliminating central buffering delays
Expected Effect : Sync precision ±3ms, latency held at 85ms, throughput 30fps stable
Risk Control :
  • GPS signal loss in tunnels causing timestamp drift
  • FPGA interpolation introducing phase distortion at high-frequency transients
  • Ethernet switch congestion under burst traffic

Problem Direction 2 :

ImproveData processing throughput
VS
ConstraintComputational resource consumption

Inspiration 1 : Cross-domain reference

Application Principle: #35 Parameter changes
Cross-domain applicability Assess applicability
System and method for metadata-driven external interface generation of application programming interfaces
Innovative Solution Refine solution

Adaptive sensor data decimation with load-responsive fusion kernel

Adjust sensor sampling rates and fusion complexity based on real-time load
How to solve :
  • Implement dynamic decimation controller that monitors CPU load every 100ms and adjusts sensor effective rates: under <60% load maintain native rates (10-100Hz), at 60-80% load decimate high-frequency sensors to 50Hz, above 80% load reduce all to 30Hz keyframes via hardware-triggered sampling
  • Deploy three-tier fusion kernel with switchable algorithms—full Extended Kalman Filter at low load (≤18W), simplified weighted covariance fusion at medium load (≤25W), direct nearest-neighbor interpolation at peak load (≤28W)—selected automatically based on 5-second rolling average CPU utilization
  • Integrate edge pre-filtering modules at each sensor using low-power microcontrollers (STM32 or equivalent, 0.5W each) to perform local outlier rejection and timestamp validation before transmission, reducing invalid data packets by 40% and central processor burden by 30%
Expected Effect : Stable 30 frames/sec at 22-28W; CPU load variance <15%; throughput degradation <5% under peak conditions
Risk Control :
  • decimation timing jitter affecting synchronization
  • fusion quality drop during algorithm switching
  • edge module communication latency

Problem Direction 3 :

ImproveFusion result reliability
VS
ConstraintProcessing latency

Inspiration 1 : Cross-domain reference

Application Principle: #11 Beforehand cushioning
Cross-domain applicability Assess applicability
Method for timing adjustment, mobile station, base station, and mobile communication system
Innovative Solution Refine solution

Hardware-level pre-validation gating for multi-sensor fusion reliability

Pre-validate sensor data quality at acquisition stage before fusion entry
How to solve :
  • Embed lightweight validation logic in each sensor's edge processor to check timestamp consistency (±10ms drift threshold), data range validity (3-sigma bounds), and frame completeness
  • reject corrupted frames within 2ms at source, preventing propagation to fusion pipeline
  • Implement hardware timestamp tagging using GPS-disciplined oscillator or IEEE 1588 PTP protocol at sensor acquisition, achieving ±1ms absolute time reference across all sensors without software buffering delays
  • Deploy fast-path bypass architecture—when pre-validation confirms all sensors within ±15ms alignment (occurs in 85% of frames), route directly to fusion with 80ms latency
  • only misaligned frames trigger ±5ms interpolation buffer (adds 45ms), averaging 87ms total latency while achieving <2% failure rate
Expected Effect : Latency 87ms avg vs 150ms; reliability <2% failure; throughput stable 30fps
Risk Control :
  • GPS signal loss in tunnels causing timestamp drift
  • sensor edge processor firmware update complexity
  • validation threshold tuning for diverse environments

Problem Direction 4 :

ImproveSystem adaptability to load variance
VS
ConstraintComputational resource consumption

Inspiration 1 : Cross-domain reference

Application Principle: #15 Dynamics
Cross-domain applicability Assess applicability
Prospective determination of processor wake-up conditions in energy buffered HVAC control unit
Innovative Solution Refine solution

Adaptive multi-tier synchronization with load-triggered precision switching

Tiered sync modes auto-switch by load
How to solve :
  • Implement three-tier synchronization modes: Tier-1 (±30ms, 12W) for light load (≤3 sensors), Tier-2 (±15ms, 20W) for nominal load (4-5 sensors), Tier-3 (±5ms, 28W) for peak load (≥6 sensors), with hardware-triggered mode transitions based on real-time sensor count and CPU utilization thresholds (50% and 80%)
  • Deploy pre-compiled configuration profiles stored in 2MB lookup tables containing buffer depths (10/25/50 frames), interpolation coefficients, and fusion kernel parameters for each tier, enabling mode switching in <50ms without runtime computation overhead
  • Integrate a lightweight monitoring daemon (0.8W, 10Hz sampling) that tracks sensor active count via GPIO interrupt counters and CPU load via hardware performance counters, triggering tier transitions through direct register writes to FPGA synchronization module, bypassing software scheduling latency
Expected Effect : Power 18-28W across all loads; adaptability achieved without manual recalibration; mode switch <50ms; failure rate <2%
Risk Control :
  • tier transition hysteresis tuning insufficient causing oscillation
  • lookup table coverage gaps for uncommon sensor combinations
  • monitoring daemon failure leading to stuck tier state

Problem Direction 5 :

ImproveData processing throughput
VS
ConstraintProcessing latency

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Processing discontiguous memory as contiguous memory to improve performance of a neural network environment
Innovative Solution Refine solution

Hardware-level pre-synchronization with distributed edge timestamping for zero-latency fusion

Deploy hardware timestamping at sensor acquisition stage to eliminate runtime buffering
How to solve :
  • Install GPS-disciplined clock modules (±1μs accuracy) at each sensor interface to embed hardware timestamps at data capture moment, eliminating software synchronization delays
  • Implement FPGA-based pre-alignment engine at sensor aggregation point that performs timestamp-driven interpolation in <5ms hardware latency, delivering pre-synchronized streams to fusion module
  • Design predictive frame scheduler that pre-fetches next sensor batch during current fusion cycle, pipelining 30 frames/sec workload with overlapping execution to maintain 80ms end-to-end latency
Expected Effect : Throughput stable at 30 fps under all loads; latency held at 82ms (vs. 150ms baseline); synchronization precision ±3ms; power consumption 18W
Risk Control :
  • GPS signal loss in tunnels causing timestamp drift
  • FPGA thermal throttling under sustained peak load
  • clock synchronization protocol compatibility across heterogeneous sensors

Problem Direction 6 :

ImproveFusion result reliability
VS
ConstraintComputational resource consumption

Inspiration 1 : Cross-domain reference

Application Principle: #3 Local quality
Cross-domain applicability Assess applicability
Reconfigurable hardware structures for functional pipelining of on-chip special purpose functions
Innovative Solution Refine solution

Sensor-level distributed synchronization with local timestamp validation

Distribute sync to sensor edge processors
How to solve :
  • Embed local timestamp validators at each sensor acquisition module (camera, lidar, radar) using low-power microcontrollers (STM32 or equivalent, 0.3W each) to perform ±5ms precision alignment locally before data transmission
  • each validator cross-references GPS-disciplined hardware clocks (accuracy ±1μs) and applies lightweight interpolation (linear for 10-30Hz sensors, cubic spline for 100Hz sources) with pre-computed coefficient lookup tables stored in 64KB flash memory
  • central fusion module receives pre-synchronized data streams with embedded quality flags (timestamp drift <5ms: pass
  • 5-20ms: warning
  • >20ms: reject), eliminating the need for centralized buffering and parallel processing—fusion pipeline operates in sequential mode at 30 frames/sec with total system power 18-22W (15W baseline + 6 sensors × 0.5W average edge processing)
Expected Effect : Failure rate <2%; power 18-22W vs 40W baseline; latency maintained at 85ms; no manual recalibration needed
Risk Control :
  • GPS signal loss in tunnels causing timestamp drift
  • edge processor firmware update complexity across distributed nodes
  • interpolation coefficient table coverage for non-standard sensor rate combinations
Patsnap Eureka Solution