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Live2026-09-01 17:38 UTC+56 todayUpdated

Virtual Qualification Debuts in Autonomous Racing at Laguna Seca

For the first time, autonomous racing teams must pass a virtual qualification in dSPACE's simulator before competing at Laguna Seca.

Overtaking maneuvers executed and validated
>500
Race event date
September 3, 2026

What Happened

The Indy Autonomous Challenge (IAC) and dSPACE have introduced a new virtual qualification process in autonomous racing. For the first time, participating teams must qualify for a race inside a dSPACE simulation environment before being allowed on a physical track. Using a software-in-the-loop (SIL) tool chain with the VEOS simulation platform, the ASM vehicle and environment simulation, and AURELION sensor simulation, teams validated vehicle behavior, race strategy, and overtaking performance in a digital twin of Laguna Seca raceway.

Key Dates
  1. September 1, 2026

    Virtual qualification introduced; all participating teams qualified for the Laguna Seca race event

  2. September 3, 2026

    Laguna Seca race event takes place

Basic Qualification Scenarios
  • Safe navigation of the complete race circuit
  • Recognition and correct response to race flags
  • Returning to the pit lane when instructed
  • Handling telemetry interruptions and communication loss
  • Maintaining safe vehicle behavior under all conditions
Advanced Capabilities Required
  • Multicar racing scenarios
  • Interactions between competing vehicles
  • Autonomous overtaking maneuvers
  • Attacker-versus-defender race situations
  • Safe decision-making in dynamic traffic situations
Autonomous overtaking maneuvers

>500maneuvers

executed and validated during the virtual qualification process

For the upcoming race, teams will be required to qualify in the dSPACE simulation environment before competing on the track. This is the first time the Indy Autonomous Challenge has brought its established competition ruleset for overtaking maneuvers to Laguna Seca, a circuit known for its significant elevation changes and challenging vehicle dynamics. The dSPACE simulator enables teams to safely and effectively stress-test their multicar path planning and decision-making systems before operating in this highly challenging environment.
Janam Sanghavi, Director of Vehicle Engineering, Indy Autonomous Challenge
We are proud to support Indy Autonomous Challenge teams by providing a realistic virtual testing environment for race preparation and qualification. Our simulation environment enables teams to validate race behavior, vehicle interactions, and overtaking strategies before taking to the track. Bringing critical testing into the virtual domain allows teams to work faster, reduce risk, and make more effective use of limited track time. We are pleased to work with the teams to advance autonomous racing technology. We wish all teams a successful race at Laguna Seca.
Robin Heveling, Project Lead for the IAC simulation solution, dSPACE

Previously from dSPACE

In August 2026, dSPACE added driver monitoring simulation to its AURELION sensor simulation platform. In May 2026, it introduced a Python-based test automation SDK for SIL and HIL simulation, making it easier to reuse tests across environments. These tools support the virtual testing now used in autonomous racing qualification.

Background drawn from MotorClaw's earlier coverage of dSPACE's official releases.

Why this matters

This shifts a key part of race preparation from the physical track into simulation, letting teams stress-test overtaking and decision-making safely. It makes autonomous racing more scalable and accessible by reducing the need for costly track time, and it shows how virtual testing can become an official part of competition.

Terms in This Story

software-in-the-loop (SIL)
A testing method where software runs in a simulated environment instead of on real hardware.
digital twin
A virtual replica of a physical object or system used for simulation and testing.
Read Original: dSPACE

Summarised from the linked release; details can be imperfect — always verify against the original source.