Autonomous Driving Patent Landscape for Transformer-Based Perception

Overview of Technical Issues:

In Transformer-based autonomous driving perception systems, the attention mechanism computing unit consumes excessive computational resources that create harmful processing delays exceeding real-time safety requirements, while the feature extraction structure provides insufficient semantic conversion under complex scenarios involving occlusions and adverse weather conditions, degrading perception accuracy; the goal is to achieve robust, real-time perception that meets automotive safety standards across all operating conditions.

Solution directions generated for this problem

Problem Direction 1 :

ImproveProcessing speed
VS
ConstraintComputational resource consumption

Inspiration 1 : Cross-domain reference

Application Principle: #2 Taking out (Extraction)
Cross-domain applicability Assess applicability
Systems and methods for navigating with safe distances
Innovative Solution Refine solution

Safety-critical region extraction for selective attention processing

Extract safety-critical regions before full attention
How to solve :
  • Deploy lightweight object detector (YOLOv8-nano, 3.2M parameters) in first stage to identify safety-critical objects (vehicles, pedestrians, cyclists) and extract bounding box regions within 8ms at 2W power consumption
  • Apply full transformer attention (12-layer ViT) only to extracted regions (typically 15-25% of frame area) in second stage, reducing attention operations from O(n²) on 1920×1080 pixels to O(m²) on aggregated 480×270 regions, completing in 35ms at 18W
  • Implement dynamic region pooling with adaptive resolution: critical regions within 30m processed at native resolution, 30-50m at 0.5× scale, background at 0.25× scale, maintaining semantic fidelity while cutting total operations by 68%
Expected Effect : Latency 43ms (<50ms target), power 20W (<30W budget), detection accuracy ≥98.5% for safety-critical objects
Risk Control :
  • lightweight detector miss rate in adverse weather
  • region boundary artifacts during object motion
  • memory bandwidth bottleneck in region extraction

Problem Direction 2 :

ImproveFeature extraction robustness
VS
ConstraintComputational resource consumption

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Just in time transcoding and packaging in ipv6 networks
Innovative Solution Refine solution

Weather-Specific Transformer Branch Routing for Robust Perception

Route inputs to specialized branches
How to solve :
  • Segment transformer into four parallel weather-specific branches (clear/rain/fog/night modules, each 6-8M parameters) instead of one universal 35M+ network
  • each branch optimized for specific degradation patterns
  • Deploy lightweight sensor metadata classifier (rain sensor, ambient light, camera histogram analysis) consuming <0.5W to route input frames to appropriate branch within 2ms latency
  • Each branch operates at 7-9W power draw with 12-18ms inference time
  • only one branch active per frame, total system power 8-10W vs 30W+ for universal architecture
Expected Effect : Power consumption -70% (8-10W vs 30W); robustness maintained across all conditions; latency <20ms per frame
Risk Control :
  • metadata classifier misrouting under transition weather
  • branch switching delay during rapid condition changes
  • per-branch validation complexity for ISO 26262

Problem Direction 3 :

ImproveSemantic representation capability
VS
ConstraintSystem architectural complexity

Inspiration 1 : Cross-domain reference

Application Principle: #6 Universality (Multi-functionality)
Cross-domain applicability Assess applicability
Translation method and system using multilingual text-to-speech synthesis model
Innovative Solution Refine solution

Unified Spatio-Temporal-Semantic Attention Kernel for Transformer Perception

Consolidate separate modules into one kernel
How to solve :
  • Replace independent spatial attention, channel attention, and temporal fusion modules with a single 4D attention kernel (H×W×C×T dimensions) that jointly processes spatial location, feature channels, and temporal sequence in one unified operation, eliminating redundant parameter sets and intermediate feature maps
  • Implement factorized 4D convolution decomposition: compute attention as sequential 1D operations (H→W→C→T) reducing complexity from O(H²W²C²T²) to O(HWCT×max(H,W,C,T)), cutting parameters by 65% while preserving full cross-dimensional interaction for semantic depth
  • Deploy shared projection matrices across all attention dimensions with dimension-specific scaling factors (αH=1.2, αW=1.2, αC=0.8, αT=1.5), reducing weight storage from 4 separate 512×512 matrices to 1 shared matrix plus 4 scalars, simplifying inference graph from 4 parallel branches to 1 sequential path with 40% fewer operations
Expected Effect : Parameters reduced 65%, memory -40%, semantic accuracy maintained ±2%
Risk Control :
  • 4D kernel numerical stability under extreme aspect ratios
  • factorization order sensitivity to scene dynamics
  • shared matrix gradient conflict during multi-task training

Problem Direction 4 :

ImproveFeature extraction robustness
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Multi-user intelligent assistance
Innovative Solution Refine solution

Pre-computed Attention Map Cache with Adaptive Retrieval for Autonomous Driving Perception

Cache pre-computed attention maps during low-load phases for real-time retrieval
How to solve :
  • During vehicle startup and highway cruising, pre-compute dense attention maps (80% spatial coverage, 64 attention heads) for 50 canonical scene layouts (intersections, lane merges, weather conditions) and store in 200MB on-chip SRAM cache with indexed retrieval latency <2ms
  • Deploy lightweight scene classifier network (5M parameters, 8ms inference) to match current frame to cached prototypes using cosine similarity threshold ≥0.85, then retrieve and apply cached attention weights instead of computing from scratch
  • For novel scenes below similarity threshold, trigger full attention computation and update cache using least-recently-used replacement policy, maintaining cache hit rate ≥75% during typical driving
Expected Effect : Latency reduced to 28ms (44% improvement), dense coverage maintained at 80%, power consumption 22W
Risk Control :
  • cache coherence under rapid scene transitions
  • scene classifier misclassification rate
  • SRAM cache size constraints on embedded platforms
Patsnap Eureka Solution