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Simulation-in-the-Loop

Updated 4 July 2025

Simulation-in-the-Loop is a testing framework that integrates simulation engines, digital twins, and physical vehicles to create realistic, reproducible, and scalable environments for autonomous driving validation.
It employs a modular ROS-based architecture to seamlessly coordinate scaled physical vehicles and AI-driven digital models, reducing cost and complexity in testing.
AI-powered digital twins trained on extensive real-world data minimize the simulation-reality gap and boost the precision of automated driving controller evaluations.

Simulation-in-the-loop (SITL) is a methodology within testing and validation frameworks for automated driving controllers that tightly couples simulation engines, digital twins, and physical vehicles to evaluate control solutions in realistic, reproducible, and scalable environments. The recent simulator described in “A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers” (2507.02313) exemplifies this approach by uniting distributed hardware, virtual environments, AI-based vehicle modeling, and integrated control in a modular architecture to facilitate efficient, high-fidelity evaluation for autonomous driving research and deployment.

1. System Architecture and Core Components

The simulator is designed as a modular, distributed system orchestrated via the Robot Operating System (ROS), supporting flexible integration of both physical and virtual vehicles:

Host Computer: Runs the Prescan virtual environment, which generates detailed dynamic scenarios (including sensor feeds and traffic interactions) and communicates via a Simulink-ROS interface.
Data Server: Stores all simulation results and raw logs (*.bag format), supporting offline analysis, machine learning (for digital twin training), and reproducible experimentation.
Targets (Vehicles):
- ViL Target: A 1/10th-scale physical car (F1tenth platform) equipped with embedded PC, sensors, and actuators; operates in an AR laboratory, tracked in real-time via a VICON motion-capture system, and receives scenario input projected onto the driving surface.
- DT Target: A digital twin running as a ROS node, either as a standard kinematic model or as a high-fidelity AI-powered deep learning model trained on real driving data.

The simulator can operate in:

ViL mode: all vehicles are physical,
DT mode: all vehicles are virtual,
Mixed mode: combination, allowing direct interaction between real cars and digital twins.

Physical and virtual vehicles communicate over ROS, ensuring tight synchronization and real-time closed-loop dynamics.

2. AI-Powered Digital Twins: Bridging the Reality Gap

A central challenge in SITL is the simulation-reality gap. Conventional physically-based models often fail to accurately replicate actuator delay, friction, actuator nonlinearity, and environmental uncertainty. This simulator incorporates AI-powered digital twins (DTs) to address these shortcomings:

Training Data: Over 92,000 samples of real-world driving data from the scaled vehicles.
Model Structure: Recursive neural networks (RNNs), capturing nonlinear temporal dependencies between command history and resulting vehicle state.
Formulation: The learning task is described by:

$\begin{align*} v_{t+1} &= v_t + \Delta_t f(u_t, v_t) \ v_T &= v_0 + \bar{\Delta} \cdot \mathbf{f}'_{\pmb{\iota}}(u_0, v_0, \ldots, u_{T-1}, v_{T-1}) \end{align*}$

where $v_t$ is vehicle velocity, $u_t$ is control input, and $\mathbf{f}'$ is an RNN mapping input sequences to precise state evolution.

Outcome: Experimental validation shows the RNN-based DTs mirror real vehicle position and velocity trajectories significantly better than kinematic models (refer to Figs. 9–11 in the paper), enabling more realistic simulation-in-the-loop controller evaluation and shortening the path to deployment.

3. Space, Cost, and Experimentation Efficiency via Scaled Physical Vehicles

The facility uses 1/10th-scale autonomous cars with on-board compute and sensors, operating in a 7×3 m AR lab. Overhead projectors can render dynamic scenarios, lane lines, traffic signals, and obstacles in real time, enabling:

Emulation of urban traffic complexity that would otherwise require thousands of square meters in a real-world test track.
Real-time motion tracking and scenario alignment using a VICON optical system, facilitating the "vehicle-in-the-loop" paradigm.
Significant reduction in infrastructure and operational costs while enabling rapid, reproducible scenario authoring and execution.

4. Controller Integration, Flexibility, and Safety

The system provides robust integration for a variety of controllers and safety mechanisms:

Supported Control Algorithms:
- Pure Pursuit: Lateral control (steering to follow a path).
- Adaptive Cruise Control: Longitudinal velocity regulation with dynamic safe distance computation: $d_\text{safe} = d_\min + v \cdot t_\text{safe}$.
- Safety Filter: Rule-based or automated synthesis (from LTL logic) for emergency braking and constraint enforcement:
$u_t = \begin{cases} v_{\mathrm{cmd}}, & d > d_{\mathrm{DET}} \ v_{\mathrm{dcl}}, & d_{\mathrm{EMR}} < d \leq d_{\mathrm{DET}} \ 0, & d \leq d_{\mathrm{EMR}} \end{cases}$
Automated Safety Synthesis: Linear Temporal Logic (LTL) specifications to automatically synthesize safety controllers, formalizing rules such as stop-at-red, yield-to-pedestrian, and emergency response.

Controllers for both physical and virtual vehicles are implemented as ROS nodes, allowing easy substitution and extension, and all vehicle actions can be logged and analyzed for repeatability and comparative benchmarking.

5. Mixed-Reality Co-Simulation: Experimental Validation

The SITL platform enables real-time, closed-loop simulation experiments combining physical cars and digital twins:

Scenario Realism: Vehicles interact with simulated pedestrians, traffic lights, and other actors, with AR projections for physical cars mirroring the digital environment.
Fidelity: RNN-based DTs track physical car trajectories in both position and velocity domains more precisely than traditional models; the “reality gap” is minimized.
Safe Controller Evaluation: Both real and virtual cars respond correctly to dynamic traffic lights, pedestrian crossings, and leading vehicles, as enforced by the safety controller logic, in both single-vehicle and multi-agent settings.
Mixed-Mode Operation: The system supports any mix of real and virtual agents, allowing scalability studies (dozens of DTs with few physical cars), as well as fundamental research into interaction policies and traffic management strategies.

6. Applications and Broader Implications

SITL as implemented in this simulator provides:

Scalable Validation: Facilitates thorough, rapid, and scalable testing of autonomous vehicle controllers—purely virtual at early stages, moving toward hardware-in-the-loop and hybrid physical/virtual validation during deployment preparation.
Research Utility: Enables safe, reproducible studies of AV behavior, policy learning, and safety evaluation across a broad spectrum of scenarios—including rare or safety-critical situations difficult to stage in full-scale environments.
Pedagogical Impact: The open-source code and datasets, along with the modular structure, support academic teaching and community extension.
Safe and Cost-Effective Development: By operating in a scaled lab with AR feedback, safety and cost barriers associated with full-scale AV testing are substantially reduced, with the ability to explore multi-agent and intelligent traffic system scenarios.

7. Technical Details and Key Formulas

Kinematic Model for Vehicle Motion: $\left\{ \begin{aligned} x_{t+1} &= x_t + \Delta_t v_t \cos(\theta_t) \ y_{t+1} &= y_t + \Delta_t v_t \sin(\theta_t) \ \theta_{t+1} &= \theta_t + \frac{\Delta_t}{l} v_t \tan(\delta_t) \ v_{t+1} &= v_t + \Delta_t a_t \ \end{aligned} \right.$

where $a_t$ is computed via PD control: $a_t = K_p (u_t - v_t) + K_d (\dot{u}_t - \dot{v}_t)$

Deep Learning Digital Twin (abstractly): $v_{T} = \mathbf{f}'(\mathbf{u}, \mathbf{v})$

where $\mathbf{u}, \mathbf{v}$ are sequences of past control and state inputs, and $\mathbf{f}'$ is learned by an RNN.

Safety Filter Logic: $u_t = \begin{cases} v_{cmd}, & d > d_{DET} \ v_{dcl}, & d_{EMR} < d \leq d_{DET} \ 0, & d \leq d_{EMR} \end{cases}$

LTL Specification (rule synthesis example): $\begin{aligned} \varphi := & \ \varphi_e \rightarrow \varphi_v \ \varphi_e := & \ (\square \lozenge \lnot \mathrm{URG}) \wedge (\square \lozenge \lnot \mathrm{WRN}) \ \varphi_v := & \ (\square \lozenge \mathrm{MOV}) \wedge (\square ((\mathrm{URG} \wedge \lnot \mathrm{WRN}) \rightarrow \bigcirc \mathrm{STP})) \ & \wedge (\square ((\mathrm{WRN} \wedge \lnot \mathrm{URG} \wedge (\mathrm{MOV} \vee \mathrm{DCL})) \rightarrow \bigcirc \mathrm{DCL})) \ & \wedge (\square (\lnot (\mathrm{URG} \vee \mathrm{WRN}) \rightarrow \bigcirc \mathrm{MOV})) \end{aligned}$

Summary Table: Simulator Modes and Features

Mode	Physical Cars	Digital Twins	Key Functions
ViL	Yes	No	Real-world-in-the-loop validation
DT	No	Yes	Scalable AI-based virtual testing
Mixed	Yes	Yes	Real and virtual agent interaction

Conclusion

The vehicle-in-the-loop simulator with AI-powered digital twins demonstrates an advanced SITL paradigm for validating automated driving controllers. By blending scaled hardware, high-fidelity neural-network digital twins, AR-enhanced scenarios, and formal safety logic, it enables rapid, realistic, and secure evaluation of both individual controllers and multi-agent intelligent traffic behaviors. This design highlights a practical path for closing the simulation-reality gap and accelerating robust AV controller deployment in both research and industrial settings.

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References (1)

A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers (2025)