Real2Sim2Real Autoware Deployment

• Real2Sim2Real Autoware Deployment is a unified methodology that combines real-world data, digital twin modeling, and high-fidelity simulation for validating AV stacks.
• It employs a four-stage pipeline—data capture, system identification, simulation, and sim-to-real adaptation—to ensure continuous calibration and performance across diverse platforms.
• The approach leverages advanced state estimation and online parameter tuning to enhance simulation fidelity and streamline the transition from virtual to physical testing.

Real2Sim2Real Autoware Deployment defines a unified methodology for developing, validating, and deploying autonomous vehicle (AV) stacks—specifically Autoware—by integrating live empirical data, digital twin modeling, high-fidelity simulation, and iterative sim-to-real adaptation. This process leverages autonomy-oriented digital twins and advanced system identification techniques to enable accurate, real-time simulation and to streamline transferability between virtual and physical testbeds across a range of vehicle scales and operational domains (Samak et al., 2024).

1. Pipeline Architecture: Unified Real2Sim2Real Workflow

The core architecture of real2sim2real Autoware deployment comprises a four-stage pipeline: (1) data capture from real-world sensors and manual/teleoperated driving, (2) online or batch system identification for digital twin generation, (3) simulation in a high-fidelity, Unity-based environment via the AutoDRIVE Simulator, and (4) sim2real adaptation through real-time parameter updates and closed-loop vehicle testing. Each stage is interconnected, allowing for continuous calibration and validation, ensuring that improvements in the simulated environment propagate to the physical vehicle, and vice versa.

Workflow steps are summarized as follows:

Module	Primary Functions	Data/Interfaces
Real-world Data Capture	Sensor logging; mapping; trajectory recording	IMU, LIDAR (2D/3D), cameras, encoders
System Identification	Parameter calibration (mass, tires, suspension, sensors)	Batch/online calibration; auto-benchmark
Simulation Engine	Physics; sensor/graphics; hardware abstraction	Python/C++/ROS/Autoware APIs
Sim2Real Adaptation	Online tuning; sim-to-real validation/closed-loop control	Physical vehicle with Autoware

This configuration facilitates extensibility across vehicle classes and operational design domains (ODDs), illustrated by deployments ranging from small-scale (Nigel, F1TENTH) to mid-/full-scale platforms (Hunter SE, OpenCAV).

2. Algorithmic and Mathematical Underpinnings

Digital twin fidelity and sim-to-real bridging are enabled by recursive system identification and advanced state estimation:

1. Online System Identification: Model parameters $\hat\theta$ are recursively updated to minimize output prediction error:

$$\hat{\theta}_{k+1} = \hat{\theta}_k + L_k\left(y_k - f\left(x_k; \hat{\theta}_k\right)\right)$$

with $x_k$ as the input/state vector, $y_k$ as measured outputs, $f(\cdot; \hat\theta)$ the twin’s forward model, and $L_k$ a gain matrix (e.g., from EKF or gradient descent).

2. State Estimation (EKF):

The digital twin maintains an internal state via an Extended Kalman Filter: - Prediction: $x_{k+1|k} = f(x_{k|k}, u_k, \hat\theta_k)$

$P_{k+1|k} = A_k P_{k|k} A_k^T + Q_k$

• Update: $$K_{k+1} = P_{k+1|k} H_{k+1}^T (H_{k+1} P_{k+1|k} H_{k+1}^T + R_{k+1})^{-1}$$ $$x_{k+1|k+1} = x_{k+1|k} + K_{k+1}(y_{k+1} - h(x_{k+1|k}))$$

  $P_{k+1|k+1} = (I-K_{k+1}H_{k+1})P_{k+1|k}$

1. Performance and Error Metrics:
   • Root-mean-square error (RMSE): $$\mathrm{RMSE} = \sqrt{\frac{1}{N}\sum_{i=1}^N (e_i)^2}$$
   • Real-time factor (RTF): $$\mathrm{RTF} = T_{\mathrm{sim}} / T_{\mathrm{real}}$$, where RTF ≈ 1 indicates real-time simulation.

3. Development of Autonomy-Oriented Digital Twins

Vehicle Modeling:

Digital twins replicate rigid-body and sprung-mass dynamics, with parameterized models for powertrain (electric or internal combustion), brakes, steering actuators (including Ackermann geometry), suspension (including anti-roll bar modeling), and tire interactions via spline-based slip-force curves. Calibration is grounded in both manufacturer data and experimental benchmarks.

Sensor Modeling:

Each sensor (encoders, IMU/GNSS, LIDAR, cameras) receives explicit stochastic and physical modeling. 3D LIDAR simulation uses multi-channel raycasting to assemble spatially accurate point clouds. Camera models employ Unity-based view/projection matrices with realistic post-processing (distortion, motion blur, bloom).

Graphical Fidelity & Calibration:

Simulation leverages lightmaps and level-of-detail (LOD) culling to balance render cost against perception system fidelity, with explicit strategies to prevent AV camera perception artifacts. All critical parameters (mass, CG, inertia, sensor alignment) are calibrated using a combination of datasheets, measurements, and dynamic testing.

4. Integration of Autoware with AutoDRIVE Ecosystem

Software Architecture:

Autoware is interfaced with the AutoDRIVE Simulator via Python/C++/ROS1/ROS2 APIs, operating on the ROS 2 Galactic distribution. Human-machine interfaces (HMI)—keyboards, gamepads, steering rigs—have identical mappings in simulation and on physical vehicles.

Message Flow and Sequence:

The following table summarizes modular communication in the pipeline using ROS 2 topics:

Module	Input Topics	Output Topics
SLAM/Mapping	/scan (LaserScan), /odom (Odometry)	/map (OccupancyGrid or PCD)
Localization	/scan, /map, /odom	/pose (PoseStamped)
Planner	/pose, /goal (PoseStamped)	/trajectory (Path)
Controller	/trajectory, /pose	/cmd_vel (Twist)
Vehicle Driver	/cmd_vel	EtherCAT/CAN bus to actuators

A typical operational sequence is: teleoperation node publishes velocity commands; sensor data flow to SLAM; maps are created and saved; waypoint trajectory servers push goals to the planner; the controller actuates motion via velocity commands.

5. Deployment, Empirical Validation, and Metrics

Comprehensive validation encompasses platform diversity, real-time performance metrics, and both on-road and off-road environments. Representative results are:

Platform	RTF (sim)	Control Latency	Track RMSE (sim/real)	Success Rate
Nigel (on-road)	1.1	15 ms	0.03 m / 0.08 m	100% (10/10)
F1TENTH (racing)	0.9	20 ms	0.04 m / 0.12 m	90% (9/10)
Hunter SE	1.0	25 ms	0.05 m / 0.15 m	80% (8/10)
OpenCAV	0.8	30 ms	0.06 m / 0.20 m	70% (7/10)

Mapping times for small-scale layouts are 5–15 s, planner update latencies range from 10–30 ms, and controller execution (pure-pursuit + PID) occurs within 20–40 ms bounds. Notably, off-road deployment (first-ever for Autoware on Hunter SE) achieved ~85% success, with digitally adaptive parameters reducing trajectory drift by ~30%.

6. Practical Challenges and Solutions

Deployment encountered technical challenges:

• Extended Autoware build times (>8 hours) and high memory consumption necessitated continuous power and isolated workspaces.
• Conflicts between ROS1/ROS2 and Autoware distributions required robust environment management, specifically colcon overlays.
• Trade-offs in graphical LOD culling versus sensor fidelity were addressed to ensure AV cameras maintained performance, despite aggressive geometry simplification.
• Processing bottlenecks in 3D LIDAR point-cloud generation prompted the development of 3D-to-2D scan projection for structured environments, thus reducing CPU load and complexity.

7. Context, Significance, and Outlook

The real2sim2real methodology for Autoware deployment establishes a replicable blueprint for achieving high-fidelity, scalable, and robust end-to-end AV workflows. The integration of autonomy-oriented digital twins and online system identification supports rapid adaptation across domains and platforms while providing empirical rigor via continuous sim-to-real validation. The first successful off-road Autoware deployment demonstrates expanded ODD coverage. This approach enables more extensive scenario coverage during virtual validation, accelerates field testing iterations, and lays a foundation for further advances in adaptive autonomy infrastructure (Samak et al., 2024).