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AlpaSim consists of several microservices that work together to create a complete autonomous vehicle simulation. Each service has a specific responsibility and communicates via gRPC.

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Service overview

The runtime is the central coordinator of the simulation.Location: /src/runtimeResponsibilities:
  • Drives the main simulation loop
  • Maintains the world state
  • Coordinates all service communications
  • Acts as a load balancer for replicated services
  • Generates synchronized logs
  • Manages simulation timing and timesteps
Communication pattern:
  • Role: gRPC client
  • Connects to: All other services
  • Must know: Addresses of all microservices
The runtime is as I/O intensive as all other services combined since it routes all communication.
The driver service runs the autonomous driving policy network.Location: /src/driverResponsibilities:
  • Executes the egovehicle policy network
  • Processes sensor inputs
  • Generates driving trajectories
  • Makes planning decisions
Communication pattern:
  • Role: gRPC server
  • Receives: Sensor data, ego state, route information
  • Returns: Planned trajectory in local frame
Coordinate frames:
  • Receives noised ego position in local frame
  • Returns waypoints in what it thinks is local frame (actually noised)
  • Runtime translates from noisy frame to true local frame
The driver typically has the highest computational requirements of all services, especially when using neural network policies.
The controller models the vehicle controller and dynamics.Location: /src/controllerResponsibilities:
  • Models vehicle controller behavior
  • Simulates vehicle dynamics
  • Provides egomotion estimates
  • Translates planned paths to vehicle actuation
Communication pattern:
  • Role: gRPC server
  • Receives: Current pose_local_to_rig, velocities, reference trajectory in rig frame
  • Returns: Future local->rig poses and estimates
Example from source:
# From src/controller/alpasim_controller/system.py
current_pose_local_to_rig = self._trajectory.last_pose.to_grpc_pose_at_time(
    current_timestamp
)

return Response(
    pose_local_to_rig=current_pose_local_to_rig,
    pose_local_to_rig_estimated=current_pose_local_to_rig,
)
The physics service applies physical constraints to keep vehicles grounded.Location: /src/physicsResponsibilities:
  • Applies ground mesh constraints
  • Ensures vehicles stay on the road surface
  • Constrains both ego vehicle and traffic actors
  • Provides physically plausible motion
Communication pattern:
  • Role: gRPC server
  • Receives: Ego and traffic poses as local -> AABB transformations
  • Returns: Constrained poses in same frame
Physics precision is a non-goal. The physics service provides “good enough” constraints to keep vehicles on roads, not high-fidelity physics simulation.
The sensorsim service uses Neural Rendering Engine (NRE) for sensor simulation.Location: Separate NRE service (integration details in /src/grpc)Responsibilities:
  • Renders camera frames using neural rendering
  • Provides high-fidelity sensor simulation
  • Simulates camera responses to world state
  • Handles multiple camera configurations
Communication pattern:
  • Role: gRPC server
  • Receives:
    • Rig trajectory in local frame
    • Per-camera calibration rig->sensor_pose
    • local->AABB trajectories for dynamic objects
  • Returns: Rendered camera images
Computational requirements:
  • Second-highest load after driver
  • Typically requires GPU acceleration
  • Benefits significantly from horizontal scaling
The traffic simulation service actuates non-ego actors.Location: Coming soonResponsibilities:
  • Simulates pedestrians, vehicles, and other actors
  • Neural traffic behavior modeling
  • Provides realistic traffic patterns
  • Actuates non-ego entities
Communication pattern:
  • Role: gRPC server
  • Receives: World state bounding boxes in local -> AABB frame
  • Returns: Updated actor positions and states
The neural traffic simulator is under development. Current versions may use simpler traffic models.
The evaluation module processes simulation logs to compute metrics.Location: /src/evalResponsibilities:
  • Processes simulation logs after completion
  • Computes autonomous driving metrics
  • Analyzes driving performance
  • Generates evaluation reports
Communication pattern:
  • Role: Standalone tool (not in simulation loop)
  • Reads: Log files produced by runtime
  • Outputs: Metrics and evaluation results
Note: Eval runs outside the main simulation loop.

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gRPC communication

All services communicate using gRPC with Protocol Buffers.

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Protocol definitions

The gRPC API is defined in /src/grpc/: 
# Example: Pose representation in gRPC
message Pose {
    Vec3 vec = 1;
    Quat quat = 2;
}

message PoseAtTime {
    Pose pose = 1;
    int64 timestamp_us = 2;
}

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Service communication pattern

  1. Runtime initiates all requests
  2. Services respond with computed results
  3. No service-to-service communication
  4. Runtime coordinates the simulation loop

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Scalability and deployment

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Horizontal scaling

Services can be replicated based on computational needs:
Single-machine deployment with all services:
# docker-compose.yml example
services:
  runtime:
    # Runtime service
  driver:
    # Driver service
    deploy:
      replicas: 1
  sensorsim:
    # Sensorsim service
    deploy:
      replicas: 3  # Scale based on load

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Service discovery

The runtime needs to be configured with service addresses:
  • Services run as daemons on known ports
  • Runtime configuration specifies service endpoints
  • No dynamic service discovery required
  • Simple and predictable networking model

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Performance considerations

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Computational load hierarchy

Typical resource requirements (highest to lowest):
  1. Driver - Neural network inference
  2. Sensorsim - Neural rendering
  3. Controller - Vehicle dynamics simulation
  4. Trafficsim - Traffic simulation
  5. Physics - Ground constraints

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Bottlenecks

  • Runtime I/O - All communication flows through runtime
  • Sensorsim rendering - GPU-intensive neural rendering
  • Driver inference - Neural network forward passes

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Optimization strategies

  • Scale expensive services (sensorsim, driver) horizontally
  • Use GPU hardware for rendering and inference
  • Optimize runtime communication patterns
  • Batch operations where possible

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