Connected and Autonomous Vehicles

We perform large-scale design and validation of path planning algorithms for connected and autonomous vehicles (CAVs).

Acting as a perfect complement to sensor testing tools and driving simulation software, Aimsun solutions can integrate seamlessly into your testing environment, providing scenario generation for both ordinary and non-compliant situations.

We offer safe, repeatable, and efficient testing of path planning on anything from a single intersection to an entire city: set up thousands of scenarios without the need for expensive field testing or laborious scripting.

Connected and Autonomous Vehicles - Case Studies


Validating Automated Driving Systems in a virtual environment


Our team provided the project’s core traffic simulation environment, used to identify areas where real-world testing would be necessary.

Who do we serve?


The team

Our industry-leading team has worked on some of the world’s most complex mobility challenges and can support your testing program, both remotely and on-site. 

We’ve accumulated know-how from billions of simulated miles in 90 countries over 24 years.

Path planning testing

In just a few hours, we can create a full typology of highway on-ramp geometries, load them with demand ranging from free-flow to gridlock, and vary the mix of driver aggression/cooperation. This synthetic generation, execution, and analysis of tens of thousands of scenarios is exponentially more efficient and wider-ranging than any methodology based on field data. 

Going beyond the capabilities of trajectory analysis or scripted scenario creation, we can analyze edge cases: traffic violations such as rolling stops, running red lights, jaywalking or speeding – even the oft-cited moral dilemma of choosing who to spare in a fatal collision. There is no need to drive around seeking the conditions that you want to test, or to laboriously script each actor’s behavior frame-by-frame: scale and speed are of the essence. Our solution provides all the rich operational complexity that comes from working in a wide-area city or highway network: broken traffic signals, blocked lanes, occupied yellow boxes and variable speed limits are an integral part of the environment.

Use cases

The scope of testing is virtually unlimited: public transport, human-driven vehicles, pedestrians, bicycles and motorcycles on highways and urban environments. The emphasis is on the scale of these virtual environments; unlike other tools, a virtual environment is not limited to a set route, has no predetermined number of actors or sequences, and can vary the testing scope without extensive and laborious intervention. 

Testing can include extraordinary scenarios with rogue actors that would be prohibitively expensive or impossible to perform in the field; you can also run wide-area regression tests to ensure that a new release of autonomy stack continues to meet prior quality standards. We’ll strive to obtain realistic estimates on overall journey time, emissions profile, energy consumption and smoothness of ride for door-to-door trips.

We work with sensor testing tools and vehicle dynamics simulation tools, such as Simcenter PreScan to provide a test harness that is full-stack, highly automated and infinitely scalable. This provides a much more varied and realistic environment for the test vehicle, mirroring real-life conditions, where drivers can’t anticipate their interactions with other vehicles or road users, or the state of traffic signals ahead.

Top features

  • Capable of seamless integration with 3D sensor simulation tools such as Simcenter PreScan, and 3D visualization engines such as Unity and Unreal Engine.
  • Customizable to automatically create wide-area simulation models from imported high definition maps, whether proprietary or standard-based.
  • Automatic synthesis of realistic background traffic demand and signal timings so that tests can be performed by simply selecting a geographic area with HD map coverage.
  • 100 Hz interfacing with the AV stack to exchange vehicle positions and communicated intentions.
  • High-fidelity, space-based behavioral models incorporating vehicle kinematics.
  • Launch, execution and control of thousands of instances on private and commercial cloud on Linux or Windows.
  • Support for direct, prescriptive and semantic definition of rogue behavior including taking direct control of actors; prescribing action sequences; and defining sets of conditions to be met.
  • Repeatable, deterministic experiments given a fixed random seed.
  • Robust, industry-leading scenario support enabling thousands of scenarios to be stored in and executed from a single file.
  • On-site configuration and project support.

CAV Blog

Taking the micro to the macro: scaling impact assessment of connected and autonomous vehicles

How can we represent microscopic simulation results at the macroscopic level? One way is by use of the Macroscopic Fundamental Diagram (MFD) concept

To learn more, send us a message and we'll start the conversation.

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Cite Aimsun Next

Aimsun Next 24

Aimsun (2024). Aimsun Next 24 User’s Manual, Aimsun Next Version 24.0.0, Barcelona, Spain. Accessed on: April. 16, 2024. [Online].


Aimsun Next 24

@manual {AimsunManual,
title = {Aimsun Next 24 User’s Manual},
author = {Aimsun},
edition = {Aimsun Next 24.0.0},
address = {Barcelona, Spain},
year = {2024. [Online]},
month = {Accessed on: Month, Day, Year},
url = {},

Aimsun Next 24

T1 – Aimsun Next 24 User’s Manual
A1 – Aimsun
ET – Aimsun Next Version 24.0.0
Y1 – 2024
Y2 – Accessed on: Month, Day, Year
CY – Barcelona, Spain
PB – Aimsun
UR – [In software]. Available: