Autonomous Driving Patent Landscape for Attention-Based Models

Overview of Technical Issues:

In attention-based autonomous driving systems, the attention computation module insufficiently guides processing resources toward safety-critical objects during complex multi-object scenarios, causing the system to distribute computational focus nearly uniformly across all detected features rather than prioritizing imminent threats; this functional insufficiency results in delayed hazard recognition with reaction latencies extending 100-300 milliseconds beyond acceptable safety margins, directly compromising collision avoidance performance in dense traffic environments.

Solution directions generated for this problem

Problem Direction 1 :

ImproveAttention weight differentiation ratio
VS
ConstraintAttention computation processing time

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

Pre-computed threat probability map matching for real-time attention allocation

Offline pre-compute threat maps for traffic patterns then runtime match scenes to templates
How to solve :
  • Offline phase: Generate threat probability maps for 50+ canonical traffic scenarios (intersections, merges, lane changes) using historical collision data and physics-based trajectory simulation, encoding distance-velocity-angle threat scores into 256×256 lookup grids with 0.5m spatial resolution
  • store maps in compressed hash tables (key: scene descriptor vector, value: threat weight array) occupying <80MB GPU memory
  • Runtime phase: Extract current scene descriptor (object count, spatial distribution, velocity field) in 3-5ms using lightweight feature hashing, retrieve closest pre-computed map via cosine similarity matching (threshold ≥0.85), directly apply stored 5-10× differentiated weights to detected objects without iterative scoring
  • Fallback mechanism: For novel scenes (similarity <0.85, ~8% occurrence), use fast rule-based threat scoring (TTC <2s → weight ×8, lateral overlap >60% → weight ×6) completing in 12-15ms, then cache result to expand map library
Expected Effect : Differentiation 5-10×, latency +8ms vs +40ms baseline, 15+ object scenes processed in 18ms
Risk Control :
  • Map coverage gaps for rare scenarios
  • hash collision causing mismatched retrieval
  • memory bandwidth bottleneck during map lookup

Problem Direction 2 :

ImproveAttention weight differentiation ratio
VS
ConstraintComputational energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #19 Periodic action
Cross-domain applicability Assess applicability
Methods and systems for generating a horizon for use in an advanced driver assistance system (ADAS)
Innovative Solution Refine solution

Adaptive threat-triggered attention escalation with dual-mode energy management

Dual-mode attention with threat-triggered escalation
How to solve :
  • Implement baseline low-precision mode (2× differentiation, INT8 quantized network at 0.6V/500MHz GPU) for routine driving, consuming 3.2W average power
  • activate high-precision mode (8× differentiation, FP16 network at 0.9V/1.2GHz) only when threat triggers fire—distance <20m, time-to-collision <3s, or lateral velocity >8m/s—detected via lightweight rule-based pre-filter operating at 1ms latency
  • Deploy predictive threat buffer maintaining rolling 500ms horizon of pre-computed attention weights for high-probability scenarios (adjacent lane vehicles, crosswalk zones) using offline-trained lookup tables indexed by object kinematics, enabling instant weight retrieval upon trigger activation without full computation overhead
  • Integrate hardware-accelerated trigger logic on dedicated 0.8W NPU performing parallel kinematic evaluation across all detected objects, generating binary threat flags before main attention module processes frame, ensuring mode switching completes within 15ms to maintain safety margins
Expected Effect : Energy consumption 4.8W average (45% reduction), 8× differentiation for threats, <50ms total latency
Risk Control :
  • trigger false negatives in edge cases
  • mode switching transient delays
  • lookup table coverage gaps for novel scenarios

Problem Direction 3 :

ImproveThreat detection response speed
VS
ConstraintAttention computation processing time

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Datapath cache
Innovative Solution Refine solution

Predictive Threat Buffer with Pre-Computed Attention Weights for Autonomous Driving

Pre-compute attention weights offline for common traffic scenarios to eliminate runtime calculation delays
How to solve :
  • Build offline threat probability database containing pre-computed attention weights for 50+ standard traffic patterns (intersections, merges, lane changes) using historical driving data and physics-based collision models
  • weights stored as lookup tables indexed by object distance (0-100m, 5m bins), relative velocity (-30 to +30 m/s, 2m/s bins), and trajectory angle (0-360°, 15° bins)
  • Deploy real-time scene matcher using lightweight feature hashing (128-bit descriptor, <5ms matching time) to identify current traffic configuration and retrieve corresponding pre-computed attention weights from database, bypassing iterative scoring for 12-18 of 15+ detected objects
  • Implement hybrid processing pipeline where 80% of objects use cached weights (2-3ms lookup), only novel or ambiguous cases (occluded objects, unusual trajectories) trigger full attention computation (25-30ms), with cache hit rate ≥85% validated through shadow-mode testing
Expected Effect : Response latency reduced from 100-300ms to <50ms; processing time per frame reduced by 120-180ms; cache hit rate 85-92% in urban scenarios
Risk Control :
  • database coverage gaps for rare scenarios
  • scene matching false positives under sensor noise
  • cache staleness during rapid environmental changes

Problem Direction 4 :

ImproveThreat detection response speed
VS
ConstraintComputational energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #19 Periodic action
Cross-domain applicability Assess applicability
Voice Actions on Computing Devices
Innovative Solution Refine solution

Event-triggered adaptive attention computation for autonomous driving threat detection

Trigger high-speed attention only when threat conditions detected
How to solve :
  • Deploy dual-mode attention architecture: baseline 10Hz monitoring at 0.6V/500MHz GPU state (3W power) for routine driving, instant boost to 30Hz/1.0V/1.5GHz (12W power) triggered by sensor events—closing distance <20m, relative velocity >8m/s, or lateral acceleration >0.3g
  • Implement hardware event detector using dedicated low-power FPGA (0.2W continuous) monitoring LiDAR/radar streams, generating interrupt signals within 5ms when threat thresholds exceeded, bypassing CPU polling overhead
  • Apply graduated response protocol: Level-1 threats (TTC 2-4s) activate medium mode 20Hz/0.8V (6W), Level-2 threats (TTC <2s) activate full 30Hz mode, with automatic 500ms decay timer returning to baseline after threat clears, ensuring energy concentration on genuine hazards
Expected Effect : Response latency <45ms for critical threats; average power reduced 52% vs continuous high-speed mode; 89% time in low-power baseline during highway driving
Risk Control :
  • false-positive triggers causing mode oscillation
  • FPGA threshold calibration across sensor types
  • thermal management during sustained high-power episodes

Problem Direction 5 :

ImproveSafety-critical object recognition accuracy
VS
ConstraintAttention computation processing time

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Technologies for processing network packets in agent-mesh architectures
Innovative Solution Refine solution

Parallel Agent-Segmented Threat Processing Architecture

Segment attention into parallel threat-specific agents
How to solve :
  • Divide attention computation into three parallel processing agents: vehicle collision agent (handles closing vehicles <30m, TTC <4s), pedestrian hazard agent (processes pedestrians within 15m lateral distance), and static obstacle agent (evaluates fixed threats)
  • each agent operates independently on dedicated GPU stream partitions with specialized scoring models optimized for specific threat signatures, eliminating sequential bottleneck of uniform processing across 15+ objects
  • Implement distributed memory fabric architecture where each agent writes threat scores directly to shared priority queue without central coordination—vehicle agent uses kinematic prediction models (position, velocity, acceleration vectors), pedestrian agent applies trajectory forecasting with 200ms lookahead, static agent employs distance-based weighting
  • scores merge via hardware-accelerated max-heap structure updated asynchronously
  • Deploy event-driven agent activation: agents enter low-power standby when no objects detected in their domain, activate within 5ms upon sensor trigger, process assigned objects in 12-18ms per agent (vs. 45-60ms sequential), achieving 5-10× threat differentiation through specialized scoring without cumulative latency—quality control via cross-validation requiring ≥95% inter-agent agreement on top-3 threats, frame-to-frame consistency check flagging >40% rank changes, and hardware watchdog ensuring per-agent cycle time <20ms with automatic fallback to simplified scoring if exceeded
Expected Effect : Recognition accuracy +35% in 15+ object scenes; processing time held at 18-22ms vs. 45-60ms baseline; threat differentiation ratio 6.5-9.2×
Risk Control :
  • agent synchronization failure causing priority conflicts
  • memory fabric contention under peak object load
  • specialized model degradation on edge-case threats

Problem Direction 6 :

ImproveSafety-critical object recognition accuracy
VS
ConstraintComputational energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #3 Local quality
Cross-domain applicability Assess applicability
Reporting beam failure
Innovative Solution Refine solution

Spatially-Adaptive Attention Zoning for Energy-Efficient Threat Recognition

Divide scene into threat zones with localized processing intensity
How to solve :
  • Partition visual field into three spatial zones: critical zone (<15m, 0-45° lateral), transition zone (15-30m, 45-90°), and background zone (>30m)
  • apply 10-layer deep attention network (FP16 precision) only to critical zone objects, 4-layer lightweight attention (INT8 quantization) to transition zone, and rule-based distance-velocity filtering (no neural processing) to background zone
  • implement dynamic zone boundary adjustment based on vehicle speed: at >80 km/h, expand critical zone to 25m and increase transition zone attention to 6 layers, maintaining safety margins while optimizing energy allocation
Expected Effect : Recognition accuracy 94% for imminent threats; GPU energy reduced 52%; latency <50ms
Risk Control :
  • zone boundary calibration drift under varying lighting
  • quantization accuracy loss in transition zone
  • real-time zone reclassification overhead
Patsnap Eureka Solution