GITNUXSOFTWARE ADVICE
Manufacturing EngineeringTop 10 Best Autonomous Vehicle Simulation Software of 2026
Autonomous Vehicle Simulation Software comparison with a ranked shortlist of top tools like dSPACE VEOS, Simulink, and CarMaker for teams.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
dSPACE VEOS
Hardware-in-the-loop execution with VEOS plant models for closed-loop testing
Built for teams verifying automated driving controls with dSPACE hardware-in-loop.
MathWorks Simulink
Editor pickSimulink Coder for generating production code from executable vehicle and control models
Built for teams validating vehicle control and sensor algorithms with executable models.
IPG Automotive CarMaker
Editor pickScenario parameterization for batch regression across routes, traffic, and environmental variations
Built for aV teams running scenario-based validation for driving functions and stacks.
Related reading
Comparison Table
The comparison table ranks leading autonomous vehicle simulation tools, including dSPACE VEOS, MathWorks Simulink, and IPG Automotive CarMaker, to support side-by-side evaluation. It compares integration depth, data model and schema design, automation and API surface for model or scenario provisioning, and admin controls such as RBAC and audit log coverage. The result highlights tradeoffs in extensibility, configuration management, and throughput across common simulation workflows.
dSPACE VEOS
closed-loopVEOS provides a simulation environment for automated driving function development with vehicle dynamics, sensor simulation, and closed-loop test integration.
Hardware-in-the-loop execution with VEOS plant models for closed-loop testing
dSPACE VEOS connects scenario-driven automated driving tests to model-based vehicle and environment dynamics. It supports software-in-the-loop and hardware-in-the-loop workflows so control algorithms can be validated against repeatable scenarios before release to real-time platforms.
A concrete tradeoff is that achieving stable, high-fidelity closed-loop results depends on building and maintaining accurate plant models and signal mappings into the dSPACE toolchain. It fits best when teams already use dSPACE development environments and need traceable test evidence across simulation and real controller execution.
- +Hardware-in-the-loop capable test workflow for control validation
- +Strong integration with dSPACE development and measurement toolchains
- +Scenario-driven simulation supports repeatable automated driving verification
- +High-fidelity interface for exchanging signals between plant and controller
- –Best results depend on established dSPACE modeling and workflow conventions
- –Scenario creation can require specialist simulation engineering effort
- –Asset and model setup overhead can slow early prototyping
Vehicle control engineers
Validate AD functions via HIL
Reduced integration surprises
Automotive system validation teams
Trace requirements to simulation signals
Faster audit-ready reporting
Show 2 more scenarios
ADAS software developers
Regression test perception control loops
Lower defect escape rate
Developers execute software-in-the-loop regressions using repeatable scenarios to catch corner-case behavior early.
Real-time controls integration teams
Stress-test plant-model mismatches
Stabilized closed-loop timing
Integration teams compare VEOS simulation outputs against dSPACE execution to tune interfaces and timing assumptions.
Best for: Teams verifying automated driving controls with dSPACE hardware-in-loop
More related reading
MathWorks Simulink
model-basedSimulink models autonomous vehicle control systems and integrates with driving scenario and sensor simulation workflows through MathWorks toolchains.
Simulink Coder for generating production code from executable vehicle and control models
Simulink stands out for model-based design workflows that connect vehicle dynamics, control, and sensor fusion into one executable architecture. It provides block-diagram modeling, hierarchical subsystems, and automatic code generation via Simulink Coder for running the same models in real-time simulation or on embedded targets.
For autonomous vehicle use, it supports co-simulation with external driving simulators, supports sensor and perception algorithms, and enables signal-based verification with scopes and logging. The largest friction comes from assembling a full AV stack and validation workflow across tools, libraries, and interface layers.
- +Executable block-diagram models for vehicle dynamics and control together
- +Strong support for model-to-code generation for deployment workflows
- +Reusable libraries for sensors, estimation, and control signal handling
- +Robust test harnesses with logging, scopes, and automated verification
- –Building a complete AV stack often requires integrating multiple specialized tools
- –Complex models can become harder to debug than code-first approaches
- –Performance tuning for large scenarios and high-rate sensors takes expertise
Vehicle controls engineers
Design control laws and plant models
Reduced integration rework
Perception and sensor algorithm teams
Co-simulate perception with sensor streams
Faster algorithm iteration
Show 2 more scenarios
Autonomy verification engineers
Run scenario-based simulation for regressions
More reliable validation results
Use logged signals and hierarchical models to compare runs and support repeatable test evidence.
Embedded software engineers
Generate deployable code from models
Consistent runtime behavior
Translate validated models into code for real-time simulation and embedded targets with consistent architecture.
Best for: Teams validating vehicle control and sensor algorithms with executable models
VTD
virtual test drivesVTD supports virtual test drives for automated driving with scenario control, traffic models, and sensor emulation.
Scenario parameterization for batch regression across routes, traffic, and environmental variations
VTD focuses on autonomous vehicle simulation with a road and traffic scenario workflow aimed at validating driving behavior against defined environmental conditions. The tool set emphasizes high-fidelity vehicle dynamics and traffic participation for testing perception and planning stacks in repeatable runs. VTD also supports scenario parameterization so teams can iterate across routes, traffic densities, and environmental variations without rebuilding every test.
- +High-fidelity vehicle and traffic simulation supports realistic driving evaluation
- +Scenario parameterization enables systematic regression testing across variations
- +Model-driven scenario workflows support repeatable validation runs
- –Scenario setup and calibration can be time-consuming for complex cases
- –Toolchain integration and configuration require strong AV engineering expertise
Best for: AV teams running scenario-based validation for driving functions and stacks
Siemens Prescan
scenario-sensorPrescan simulates automated driving environments with high-fidelity traffic, perception, and sensor models for algorithm and system testing.
Prescan sensor simulation tightly coupled with scenario traffic and ego motion for end-to-end testing
Siemens Prescan stands out for strong model-to-maneuver workflows built around traffic, sensors, and scenario-based testing for automated driving. Core capabilities include scenario authoring, 3D virtual world setup, sensor simulation, and data generation for perception and validation tasks.
It also supports closed-loop simulation where ego motion and environmental behavior stay synchronized through the driving stack. These strengths make it a practical simulator for validating perception and control under varied traffic and sensor conditions.
- +Scenario-based testing with coordinated traffic, ego dynamics, and sensors
- +High-fidelity sensor simulation for camera, radar, and LiDAR workflows
- +Tools support repeatable data generation for regression testing
- –Setup complexity can be high for advanced worlds and sensor configurations
- –Workflow learning curve slows teams without prior simulation experience
- –Integration work is often needed to connect external perception or control stacks
Best for: Automotive simulation teams needing scenario-driven sensor validation and repeatable regression data
Autoware Foundation Autoware
open-source stackAutoware provides an open autonomous driving software stack that supports simulation-based development using common ROS ecosystems.
Autoware’s modular planning and control stack supports end-to-end autonomy simulation runs
Autoware Foundation Autoware stands out for coupling an open-source autonomous driving software stack with simulation workflows used for perception, planning, and control validation. It supports driving simulation through ROS-centric tooling and integrates with common simulators for scenario-based testing.
Core capabilities include modular autonomy components, ROS message interoperability, and reproducible evaluation pipelines built for robotics research. This makes it a strong fit for testing autonomy behaviors under controlled environments rather than only visualizing traffic.
- +Modular autonomy stack covers perception, planning, and control workflows
- +Strong ROS integration enables realistic message-level simulation coupling
- +Scenario-based testing supports repeatable autonomy evaluation runs
- +Community-driven development accelerates feature coverage across modules
- –Setup and integration require ROS and robotics tooling fluency
- –Simulator configuration takes time to align sensors, topics, and timing
- –Debugging failures often needs deep system knowledge across modules
Best for: Research teams validating autonomy logic in ROS-based simulation environments
Unity
game-simUnity enables interactive simulation and synthetic sensor data generation for autonomous driving scenarios using custom simulation pipelines.
Unity’s C# scripting and physics engine for custom sensor emulation and scenario logic
Unity stands out with a highly extensible real-time 3D engine and a massive ecosystem of assets and tooling. For autonomous vehicle simulation, it supports physics, sensing, and scenario playback through custom logic and integrations with external simulation stacks.
Developers can build sensor emulation and evaluation pipelines using C# scripting, while rendering and performance tuning are handled through Unity’s graphics and profiling tools. Complex driving scenes typically require significant engineering to connect simulation fidelity, perception ground truth, and scalable data collection.
- +Real-time rendering plus physics supports detailed driving scene construction
- +C# scripting enables custom sensor models and evaluation metrics integration
- +Large asset and plugin ecosystem accelerates environment and tooling development
- +Built-in profiling and optimization tools help sustain simulation performance
- –Autonomous-specific simulation features require substantial custom integration work
- –High-fidelity multi-sensor setups can be expensive to implement and tune
- –Deterministic, large-scale scenario generation needs extra engineering effort
- –Workflow complexity rises quickly with advanced rendering and domain tooling
Best for: Teams building custom autonomous driving simulation with strong visualization needs
CARLA
open-source simulatorCARLA simulates urban driving environments with configurable maps, traffic, and sensor setups for autonomous driving research and testing.
Town-scale scenario generation with controllable traffic and sensor ground-truth
CARLA stands out for its high-fidelity urban driving simulation that supports controllable sensors, traffic participants, and weather conditions. It enables closed-loop testing by running an autonomous stack in simulation while providing synchronized ground-truth data for perception and planning.
The simulator includes HD map support, multi-agent scenarios, and standard vehicle and sensor models used for research prototypes. CARLA is strongest for experiments that require reproducible scenarios rather than only offline rendering.
- +Synchronized sensors and ground-truth data support perception and planning evaluation
- +Urban HD maps with traffic agents enable realistic closed-loop scenario testing
- +Deterministic scenario workflows improve reproducibility across repeated experiments
- –Setup and simulation tuning can be complex across compute and sensor configurations
- –Performance tuning is often needed to keep real-time rates with many agents and sensors
- –Extending vehicle dynamics or sensor models requires engineering effort
Best for: Research teams testing perception and planning with reproducible urban driving scenarios
VTD
virtual test drivesVTD supports virtual test drives for automated driving with scenario control, traffic models, and sensor emulation.
Scenario parameterization for batch regression across routes, traffic, and environmental variations
VTD focuses on autonomous vehicle simulation with a road and traffic scenario workflow aimed at validating driving behavior against defined environmental conditions. The tool set emphasizes high-fidelity vehicle dynamics and traffic participation for testing perception and planning stacks in repeatable runs. VTD also supports scenario parameterization so teams can iterate across routes, traffic densities, and environmental variations without rebuilding every test.
- +High-fidelity vehicle and traffic simulation supports realistic driving evaluation
- +Scenario parameterization enables systematic regression testing across variations
- +Model-driven scenario workflows support repeatable validation runs
- –Scenario setup and calibration can be time-consuming for complex cases
- –Toolchain integration and configuration require strong AV engineering expertise
Best for: AV teams running scenario-based validation for driving functions and stacks
PanoSim
synthetic dataPanoSim generates simulation scenarios and sensor data for autonomous driving validation workflows.
Map-driven scenario creation with sensor replay workflows for deterministic autonomous driving evaluation
PanoSim stands out by combining map-based scene setup with simulation workflows designed for autonomous driving evaluation. It supports scenario creation, sensor simulation, and replay style runs that help teams test perception and planning stacks against consistent environments.
The tool emphasizes iterative experiment management so changes to routes, weather, and traffic conditions can be compared across runs. Its strongest fit is closed-loop AV testing where visual evidence and scenario repeatability matter.
- +Scenario and route setup centered on map workflows for repeatable AV tests
- +Sensor simulation and run management support comparative evaluation across iterations
- +Visual replay outputs make debugging perception and behavior faster
- +Experiment organization helps track changes between scenario variants
- –Integration depth with custom autonomy stacks can require additional engineering
- –Advanced customization of complex traffic behaviors is slower than expected
- –Scenario debugging requires extra iteration to isolate configuration issues
Best for: Teams running repeatable AV scenario tests with visual sensor replay and scenario tracking
RidgeRun gstreamer-applications
data pipelineGStreamer pipelines support reproducible media and sensor data processing in simulation and playback setups for autonomy validation.
Prebuilt GStreamer application workflows for assembling real-time media graphs
RidgeRun gstreamer-applications stands out for providing ready-to-use GStreamer based components that fit naturally into robotics video and sensor pipelines. Core capabilities include camera, video processing, and streaming workflows built on the GStreamer ecosystem, which supports low-latency media graph construction.
For autonomous vehicle simulation, it serves best as the transport and processing layer that can ingest recorded or simulated sensor streams and feed them into downstream perception and visualization tools. The project focuses more on media pipeline building blocks than on end-to-end autonomous driving scenario orchestration.
- +GStreamer pipeline building blocks for sensor and video streaming workflows
- +Supports modular composition of media processing stages within autonomous simulation setups
- +Reuses standard GStreamer elements for filters, muxing, and transport
- –Not an autonomous driving scenario simulator with built-in world and actor management
- –Requires GStreamer familiarity to tune caps, latency, and pipeline synchronization
- –Limited AV specific integrations like map, routing, and vehicle dynamics
Best for: Teams integrating simulated sensor and video streams into GStreamer-centric AV pipelines
Conclusion
After evaluating 10 manufacturing engineering, dSPACE VEOS stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Autonomous Vehicle Simulation Software
This buyer's guide covers autonomous vehicle simulation software from dSPACE VEOS, MathWorks Simulink, IPG Automotive CarMaker, Siemens Prescan, Autoware Foundation Autoware, Unity, CARLA, VTD, PanoSim, and RidgeRun gstreamer-applications.
It focuses on integration depth, data model fit, automation and API surface, and admin and governance controls that affect traceability, repeatability, and controlled execution across scenarios and test runs. It also connects the choice to concrete workflow strengths like dSPACE VEOS hardware-in-the-loop, Simulink Coder model-to-code generation, and CarMaker scenario parameterization for regression sweeps.
Autonomous vehicle simulation platforms that run scenario truth through control, sensor, and validation loops
Autonomous vehicle simulation software executes road and traffic scenarios while simulating ego dynamics and sensors, then feeds those signals into perception, planning, and control stacks for repeatable evaluation. It solves problems like regression testing across route and traffic variations, closed-loop validation with synchronized ground truth, and data generation for perception verification. Tools like dSPACE VEOS focus on connecting scenario-driven tests to vehicle and environment dynamics with hardware-in-the-loop workflows.
MathWorks Simulink focuses on executable block-diagram vehicle and control models, then supports co-simulation and signal-based verification through logging and scopes. The same category also includes scenario-centric simulators like IPG Automotive CarMaker and Siemens Prescan, which provide scenario execution plus sensor simulation tightly coupled to scenario traffic and ego motion.
Evaluation criteria that map to integration, automation, and governed test execution
Integration depth determines whether the simulator can exchange signals and data with external control, perception, and measurement toolchains without custom glue for every scenario. Automation and API surface determine whether scenario creation, batch runs, and evidence capture can run unattended with predictable throughput.
A data model decision determines how scenario state, sensor outputs, and ground-truth timing get represented across tools so regression runs compare like-for-like. Admin and governance controls determine how teams separate responsibilities, manage access, and maintain auditability of scenario and run configurations.
Hardware-in-the-loop closed-loop execution with plant models
dSPACE VEOS provides a hardware-in-the-loop execution workflow using VEOS plant models for closed-loop testing, which supports control validation against repeatable scenarios. This matters when the validated controller runs on real-time hardware and must stay synchronized with simulated ego motion and environment signals.
Executable model-to-code workflow for control and vehicle dynamics
MathWorks Simulink supports model-to-code generation via Simulink Coder so the same executable vehicle and control model can move toward embedded targets. This matters when the simulation is part of a deployment path and debugging must align with production execution semantics.
Scenario parameterization for batch regression across routes, traffic, and conditions
IPG Automotive CarMaker and VTD support scenario parameterization for systematic regression testing across route geometry, traffic density, and environmental variations. This matters when automated acceptance depends on running many scenario variants while keeping scenario repeatability for evidence comparisons.
Sensor simulation tightly coupled to scenario traffic and ego dynamics
Siemens Prescan couples sensor simulation with scenario traffic and ego motion so perception and control can be validated under coordinated end-to-end conditions. This matters when sensor outputs and world state must remain synchronized for camera, radar, and LiDAR workflows.
Synchronized ground truth and deterministic urban scenario reproducibility
CARLA provides synchronized sensors and ground-truth data for perception and planning evaluation, with deterministic scenario workflows that improve reproducibility across repeated experiments. This matters when evaluation depends on comparable truth states across sensor and multi-agent runs.
Data orchestration extensibility via simulation stack integration
Autoware Foundation Autoware targets ROS-centric message interoperability for modular end-to-end autonomy simulation runs. Unity targets custom sensor emulation and scenario logic via C# scripting and its physics engine, while RidgeRun gstreamer-applications targets prebuilt GStreamer components for camera and sensor media graphs.
A decision path for matching simulation workflows to governed automation needs
First, match the execution target to the integration depth required for the controller and sensor chain. dSPACE VEOS fits controller validation workflows that require hardware-in-the-loop with VEOS plant models, while MathWorks Simulink fits executable model validation and model-to-code paths.
Second, match the scenario workflow to the regression shape. CarMaker and VTD emphasize scenario parameterization for batch sweeps, while Siemens Prescan emphasizes scenario-driven sensor validation with coordinated ego and traffic motion.
Choose the execution mode that matches the controller reality
Select dSPACE VEOS when control algorithms must be validated against repeatable scenarios using hardware-in-the-loop execution with VEOS plant models. Select MathWorks Simulink when executable vehicle and control models must run in simulation with robust logging and then move toward code generation with Simulink Coder for deployment alignment.
Map scenario and regression needs to scenario parameterization strengths
Pick IPG Automotive CarMaker or VTD when regression requires batch parameterization across routes, traffic density, and environmental variations without rebuilding the scenario harness. Pick PanoSim when map-driven scenario creation and sensor replay workflows support deterministic autonomous driving evaluation with visual replay and scenario tracking.
Verify sensor fidelity coupling to scenario truth and timing
Choose Siemens Prescan when sensor simulation needs tight coupling to scenario traffic and ego motion so sensor outputs remain synchronized with end-to-end behavior under coordinated conditions. Choose CARLA when synchronized sensors and ground-truth data drive perception and planning evaluation with deterministic scenario reproducibility.
Confirm data model fit for end-to-end autonomy stack integration
Choose Autoware Foundation Autoware when ROS message-level interoperability is the integration contract for modular perception, planning, and control simulation runs. Choose Unity when custom sensor models and evaluation metrics require C# scripting and physics-driven scenario construction tied to a real-time 3D engine.
Assess automation and integration surface for unattended runs and evidence capture
Favor tools that already support the automation shape in the workflow, such as CarMaker and VTD for systematic regression sweeps using parameterized scenarios. Use RidgeRun gstreamer-applications when the automation focus is ingesting recorded or simulated camera and sensor streams through modular GStreamer pipelines feeding downstream perception and visualization.
Which teams benefit from specific autonomous vehicle simulation software capabilities
Autonomous vehicle simulation software fits teams that need scenario repeatability, synchronized sensor outputs, and controlled signal exchange between simulated worlds and autonomy software. The right tool depends on whether the priority is controller execution on real hardware, executable model validation, or scenario-scale regression.
Teams that already operate within dSPACE development environments should evaluate dSPACE VEOS for traceable closed-loop evidence, while teams building executable control models should evaluate MathWorks Simulink for Simulink Coder code generation alignment.
Control validation teams running hardware-in-the-loop evidence
dSPACE VEOS matches hardware-in-the-loop workflows by executing closed-loop tests using VEOS plant models, which supports control validation against repeatable scenarios.
Model-based design teams validating vehicle dynamics and control as executable architectures
MathWorks Simulink fits executable block-diagram models with robust test harnesses for logging and scopes and supports Simulink Coder for model-to-code generation.
Regression and scenario-sweep teams that parameterize routes, traffic, and conditions
IPG Automotive CarMaker and VTD support scenario parameterization for batch regression so algorithm versions can be compared on consistent scenario sets without rebuilding harnesses.
Automotive teams needing sensor validation with synchronized scenario traffic and ego motion
Siemens Prescan supports tightly coupled sensor simulation with scenario traffic and ego dynamics so camera, radar, and LiDAR validation can run as coordinated end-to-end testing.
ROS-based autonomy researchers running modular perception, planning, and control simulations
Autoware Foundation Autoware fits ROS ecosystem workflows by providing modular autonomy components and ROS message interoperability for end-to-end simulation runs.
Failure modes that derail integration, automation, and repeatability
Tool selection often fails when scenario fidelity and integration effort are treated as interchangeable. Multiple tools depend on up-front modeling, calibration, or stack alignment to produce stable and comparable outcomes.
Another common failure mode is building a validation pipeline around the simulator without securing the signal exchange and timing contract needed for closed-loop synchronization and evidence capture.
Choosing a high-fidelity HIL workflow without committing to accurate plant modeling and signal mappings
dSPACE VEOS achieves stable, high-fidelity closed-loop results only when plant models and signal mappings into the dSPACE toolchain are maintained, so the early investment must cover modeling and mappings before scaling scenario coverage.
Treating scenario setup as a one-time task instead of an ongoing calibration pipeline
IPG Automotive CarMaker, VTD, and PanoSim require scenario setup and calibration effort for complex cases, so timelines should include iterative scenario tuning and configuration debugging rather than assuming ready-to-run coverage.
Assuming a simulator will automatically fit an existing AV stack without integration work
CARLA and Unity can require engineering effort to extend vehicle dynamics or sensor models and to keep performance consistent across compute and sensor configurations, while Autoware Foundation Autoware requires ROS tooling fluency to align sensors, topics, and timing.
Using media streaming components as a substitute for scenario orchestration
RidgeRun gstreamer-applications provides GStreamer pipeline building blocks for camera and sensor streaming workflows but does not act as an autonomous driving scenario simulator, so scenario world and actor management still need dedicated orchestration elsewhere.
Building very large models without a debugging strategy for performance and correctness
MathWorks Simulink models can become harder to debug than code-first approaches, and performance tuning for large scenarios and high-rate sensors takes expertise, so model structure and verification harnesses must be planned to control throughput.
How We Selected and Ranked These Tools
We evaluated dSPACE VEOS, MathWorks Simulink, IPG Automotive CarMaker, Siemens Prescan, Autoware Foundation Autoware, Unity, CARLA, VTD, PanoSim, and RidgeRun gstreamer-applications using three criteria tied to real engineering workflows. Each tool received a score for features, a score for ease of use, and a score for value, and the overall rating used a weighted average where features carried the most weight at 40% while ease of use and value each contributed 30%. This editorial research used the provided tool capabilities, pros and cons, and numeric ratings to keep the ranking criteria consistent across a scenario simulator like CarMaker and a controller-code path like Simulink.
dSPACE VEOS set itself apart by delivering hardware-in-the-loop execution with VEOS plant models for closed-loop testing, and that capability lifted the features score while matching teams that need traceable signal-level evidence across simulation and real controller execution.
Frequently Asked Questions About Autonomous Vehicle Simulation Software
Which tools cover both vehicle dynamics and scenario-driven traffic interaction for closed-loop AV testing?
What are the main differences between Simulink and dSPACE VEOS for model-based AV control validation?
How do scenario authoring workflows compare across Prescan, CarMaker, and CARLA?
Which platforms integrate most directly with ROS-based autonomy stacks and ROS messaging?
What integration approach fits teams that need deterministic data generation with repeatable ground-truth logs?
Which tools are best suited for sensor simulation and perception validation rather than only traffic visualization?
How do teams usually connect external driving simulators to a control model workflow?
What extensibility model matters most when simulation needs custom sensor emulation or custom scene logic?
When scenario regression requires batch parameter sweeps without rebuilding the test harness, which tools handle it best?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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