# ICRA autonomous racing / AutoDRIVE
Source: https://kaustubhkr.github.io/projects/autonomous-racing.html

← Flagship work (https://kaustubhkr.github.io/work.html) 01 / Autonomous systems 2025–2026

# Autonomous racing at ICRA

Built the controller that placed third in ICRA 2025 simulation qualification: 81.32 seconds, zero collisions.
System ICRA autonomous racing / AutoDRIVE
My contribution Sole builder · controller, training, integration & submission
Access Private implementation

Engineering & evidence MPCC comparison, modified PPO and SAC, multi-head attention encoding and a ROS 2 training-to-control pipeline.
Official results and qualification footage. Sole builder; final time-attack placement: 14th.

Media: https://kaustubhkr.github.io/assets/icra-qualification.mp4
ICRA 2025 RoboRacer Sim Racing League · qualification footage excerpt

81.32 s Qualification run
0 Collisions
8.03 s Best lap

Competition record

## Built and submitted independently.

I designed, trained, integrated, evaluated and submitted the controller. The 2025 competition stack covered the ROS 2/Gymnasium bridge, observations, steering–throttle actions, rewards and checkpoints. ONNX, TensorRT and Triton inference tooling followed in 2026.
Qualification · 3rd 81.32 s total · 0 collisions · 8.03 s best lap.

Final time attack · 14th A separate run on a different track.

Placed ahead of entries from Autonomous Racing at Penn and the Autoware Foundation in qualification, using the official collision-adjusted standings.
Official results & full standings ↗ (https://autodrive-ecosystem.github.io/competitions/roboracer-sim-racing-icra-2025/#results)
Technical deep dive Architecture, implementation & tradeoffs
System architecture MPCC / attention-based reinforcement learning

### Classical optimisation × learned control

- 01

#### Compare

MPCC · modified PPO / SAC Compare model-predictive contouring control with policy-optimisation and actor–critic variants.

- 02

#### Encode

multi-head attention Build the learned observation representation inside the private racing stack.

- 03

#### Integrate

ROS 2 ↔ controller Connect observations, actions, reward signals, training and checkpoint evaluation.

- 04

#### Export

ONNX → TensorRT / Triton Extend policy inference with export, engine-build and serving adapters after the competition.

↖ Controller design, representation learning and execution were part of the same independently built AutoDRIVE project.
Controllers MPCC comparison · modified PPO and SAC
Representation Multi-head attention encoder
Competition Third in qualification · separate final time-attack result

01 / Implementation

## Controller comparison & learned representation

- Compared Model Predictive Contouring Control (MPCC) with modified PPO and SAC controllers in the same autonomous-racing project.

- Built a multi-head attention encoder as part of the learned-control work.

- Developed the controller variants and encoder in a private local codebase. The retained PPO baseline uses a separate, explicit observation/action contract.

02 / Implementation

## The retained PPO baseline

- Wrote the ROS 2 environment bridge around LiDAR, collision and lap feedback, with steering and throttle command publishers.

- The retained baseline includes 180 LiDAR features and 25 discrete steering–throttle combinations, with lower-throttle turning actions and higher-throttle straight actions.

- Maintained additional observation and factored-action experiments as the training interface evolved.

03 / Implementation

## The training system

- Experimented with collision penalties, survival terms, throttle incentives and lap feedback. The reward defines what the agent is encouraged to do; lap outcomes establish what it actually did.

- Built checkpoint resume, evaluation and training variants around Stable-Baselines3 PPO.

- The accompanying manuscript investigates compact LiDAR policies, observation aggregation and reward shaping. I am its first author; it is not published.

04 / Implementation

## The inference continuation

- In 2026, added ONNX export, TensorRT engine-build tooling and Triton serving adapters. These extensions came after the 2025 competition.

Engineering note

## The control contract shapes the policy.

Compressing a scan changes which geometry the agent can observe. Discretising steering and throttle changes the manoeuvres it can express. Rewarding throttle commands changes the optimisation target without directly measuring speed. I treated these choices as part of the controller, alongside the network and optimiser.

Project context Simulation competition. Third in qualification; the entry later finished 14th in the final time-attack race. The two rounds evaluate different runs and tracks.
Official competition results ↗ (https://autodrive-ecosystem.github.io/competitions/roboracer-sim-racing-icra-2025/#results)

Keep exploring

## Learned control. On a real vehicle.

↗ (https://kaustubhkr.github.io/projects/golf-cart.html) Physical golf-cart autonomy

END OF SECTION 6: projects/autonomous-racing.html