Autonomous Driving Multi-Agent Interaction Modeling for Urban Intersections

Overview of Technical Issues:

At urban intersections, the prediction module insufficiently forecasts coupled multi-agent behaviors where vehicles' decisions dynamically influence each other, and the interaction protocol insufficiently coordinates conflicting intentions among agents, resulting in either overly conservative maneuvers that reduce traffic efficiency or unsafe decisions during simultaneous yield scenarios; the goal is to develop accurate interaction models that capture game-theoretic dependencies and enable safe, efficient autonomous navigation through complex intersection conflicts.

Solution directions generated for this problem

Problem Direction 1 :

ImprovePrediction model complexity
VS
ConstraintComputational processing time

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Simplified inter-satellite link communications using orbital plane crossing to optimize inter-satellite data transfers
Innovative Solution Refine solution

Spatial zone-parallel game-theoretic prediction for real-time intersection navigation

Partition intersection into spatial zones for parallel processing
How to solve :
  • Divide intersection into 4 spatial zones (20m×20m grid) based on conflict topology
  • assign independent game-theoretic solvers to each zone running in parallel on multi-core GPU (NVIDIA Xavier or equivalent)
  • Boundary agent synchronization: agents within 5m of zone borders share state via lightweight message passing (3ms overhead), merge predictions only for cross-zone interactions using pre-computed influence matrices
  • Dynamic zone activation: activate computation only for zones containing ≥2 agents, idle zones consume <2% baseline power
Expected Effect : Computation time reduced to 65-75ms; maintains full game-theoretic coupling accuracy; processing load scales with active conflict density not total scene
Risk Control :
  • zone boundary artifacts during high-speed transitions
  • synchronization latency between parallel solvers
  • load imbalance when agents cluster in single zone

Problem Direction 2 :

ImprovePrediction model complexity
VS
ConstraintSystem energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #2 Taking out
Cross-domain applicability Assess applicability
Linear ball guided voice coil motor for folded optic
Innovative Solution Refine solution

Offload game-theoretic prediction to roadside edge computing via V2X

Extract multi-agent prediction to roadside edge units
How to solve :
  • Deploy roadside edge computing units at intersections to run full game-theoretic multi-agent models (NVIDIA Jetson AGX Orin, 275 TOPS)
  • transmit predicted trajectories and conflict probabilities to vehicles via DSRC/C-V2X at 10 Hz (100ms intervals), reducing onboard computation by 60-70%
  • Vehicle retains only local safety validation layer (lightweight collision geometry check, 8-12 ms per cycle, consuming ≤15 W) to verify received predictions against real-time sensor data and execute emergency override if discrepancy exceeds 0.8m positional error or 2 m/s velocity deviation
  • Roadside units pre-compute interaction scenarios for all approaching vehicles within 80m radius using shared Nash equilibrium solvers, amortizing computational cost across 4-8 simultaneous agents, achieving per-vehicle equivalent energy reduction of 65% compared to independent onboard processing
Expected Effect : Onboard energy -65%, prediction accuracy maintained at 94-96%, latency 100-120ms
Risk Control :
  • V2X communication dropout or delay beyond 150ms
  • roadside unit coverage gaps in non-equipped intersections
  • cybersecurity vulnerability in transmitted trajectory data

Problem Direction 3 :

ImproveDecision response speed
VS
ConstraintComputational processing time

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Data processing performance enhancement for neural networks using a virtualized data iterator
Innovative Solution Refine solution

Pre-computed Scenario Template Library for Intersection Decision Acceleration

Offline library stores solved game patterns
How to solve :
  • Build offline scenario template library containing 500+ pre-solved intersection game-theoretic patterns (left-turn vs straight, simultaneous yield, multi-agent conflicts) with verified safe maneuvers and probability distributions
  • Runtime matching engine uses lightweight feature extraction (vehicle positions, velocities, headings within 30m radius) to identify current situation and retrieve closest template in 15-20ms via k-d tree indexing
  • Deviation computation module calculates only the delta between current state and template baseline using simplified 2-agent models for critical conflicts, requiring 25-35ms instead of full multi-agent simulation
Expected Effect : Decision cycle 40-55ms total, 60% faster; safety failure rate <2.5%; template match accuracy ≥92%
Risk Control :
  • template library coverage gaps in rare scenarios
  • feature extraction misclassification under sensor noise
  • deviation model accuracy degradation

Problem Direction 4 :

ImproveNavigation safety reliability
VS
ConstraintComputational processing time

Inspiration 1 : Cross-domain reference

Application Principle: #11 Beforehand cushioning
Cross-domain applicability Assess applicability
Data privacy awareness in workload provisioning
Innovative Solution Refine solution

Pre-certified safety fallback library for intersection conflict resolution

Pre-certify conservative fallback maneuvers offline for all intersection scenarios
How to solve :
  • Build offline safety fallback library containing pre-verified conservative maneuvers (full stop, yield-to-all, emergency deceleration) for 500+ intersection topology types, each certified through exhaustive multi-agent simulation (10,000+ scenarios per topology) to guarantee zero-collision outcomes under worst-case agent behaviors
  • Deploy real-time confidence monitor (8ms execution) that evaluates game-theoretic solver output against three criteria: computation time <100ms, trajectory smoothness index >0.85, minimum safety margin ≥2.5m
  • if any criterion fails, immediately execute pre-certified fallback from library matched to current intersection type
  • Implement dual-layer verification: primary game-theoretic solver runs with 95ms time budget
  • parallel lightweight geometric collision checker (12ms) validates output
  • disagreement triggers instant fallback selection (5ms lookup) from pre-certified library, ensuring total decision cycle ≤100ms with guaranteed safety
Expected Effect : Failure rate <2%, decision time ≤100ms, 98% scenarios use optimized solution
Risk Control :
  • fallback library coverage gaps for rare intersection types
  • confidence threshold calibration sensitivity
  • fallback transition smoothness affecting passenger comfort

Problem Direction 5 :

ImproveTraffic flow efficiency
VS
ConstraintSystem energy consumption

Inspiration 1 : Cross-domain reference

Application Principle: #5 Merging
Cross-domain applicability Assess applicability
Reconfigurable hardware structures for functional pipelining of on-chip special purpose functions
Innovative Solution Refine solution

Unified multi-function computing pipeline for intersection traffic coordination

Merge traffic optimization with existing vehicle functions in unified pipeline
How to solve :
  • Integrate multi-agent coordination, sensor fusion, and path planning into a single GPU pipeline with time-sliced execution — eliminates redundant data transfers and duplicate computations across modules, reducing total energy by 30-40%
  • Deploy shared memory architecture where intersection prediction (50ms), object tracking (20ms), and trajectory optimization (30ms) access common scene representation without re-parsing sensor data — cuts memory bandwidth consumption by 60%
  • Implement dynamic workload merging — when approaching intersections (within 80m), combine game-theoretic agent modeling with routine obstacle avoidance using unified neural network backbone, processing both tasks in 90ms total versus 150ms sequential — maintains throughput efficiency while energy stays within baseline +5%
Expected Effect : Energy consumption +5% vs +20% separate; throughput efficiency 95% of human-level; processing latency 90ms
Risk Control :
  • GPU scheduling conflicts between merged tasks
  • shared memory race conditions during concurrent access
  • thermal throttling under sustained unified workload

Problem Direction 6 :

ImproveDecision response speed
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Streaming application environment with remote device input synchronization
Innovative Solution Refine solution

Pre-computed scenario library with incremental runtime adaptation for rapid intersection decision

Pre-solve intersection scenarios offline then adapt online
How to solve :
  • Build offline scenario library containing 500+ pre-solved game-theoretic intersection patterns (left-turn vs straight, 4-way yield, T-junction conflicts) with verified safe maneuver sequences, each tagged by agent count, velocity range, and spatial configuration
  • At runtime, deploy fast pattern matcher (15ms) using LiDAR-derived scene descriptors (agent positions, headings, speeds) to retrieve closest library match via k-d tree spatial indexing, achieving 92% match confidence threshold
  • Execute incremental delta solver (35ms) that takes matched template solution and adjusts only for deviations exceeding 0.5m position or 1m/s velocity difference, using gradient-based optimization on pre-computed Jacobian matrices—total decision cycle 50ms while maintaining full game-theoretic rigor for novel situations
Expected Effect : Decision time 50ms (75% reduction); safety failure rate 1.8% (85% improvement); library covers 94% of real intersection scenarios
Risk Control :
  • Library coverage gaps in rare scenarios
  • pattern matching false positives under occlusion
  • delta solver divergence for large deviations
Patsnap Eureka Solution