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Ilya Shimchik

Principal Investigator · Team Principal, Constructor Racing, Constructor Labs

Ilya Shimchik is Team Principal of Constructor Racing, leading Constructor’s autonomous racing program in the Abu Dhabi Autonomous Racing League (A2RL) at Yas Marina Circuit. His work spans the full self-driving stack — perception, state estimation, planning, and control — under the extreme conditions of wheel-to-wheel autonomous racing at speeds up to 300 km/h. He focuses on translating racing-grade autonomy into deployable Physical AI products for road and private-area domains, in close collaboration with Constructor University Bremen and the robotics group of Prof. Andreas Birk.

Key achievements

  • Led Constructor Racing to the championship title in Roborace Season Beta (2022).
  • Led Constructor Racing to P2 overall at the Abu Dhabi Autonomous Racing League 2024 (F1 Yas Marina Circuit).

Industry collaborations and partnerships

Drako Motors (ADAS / emergency obstacle-avoidance development); ASPIRE / A2RL (Autonomous racing league); Corpay (race partner); Constructor University Bremen (academic R&D partner).

Email: is@constructor.tech
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Research Portfolio

Ilya Shimchik’s expertise sits squarely within Constructor Labs’ Robotics & Autonomous Machines direction. As Team Principal of Constructor Racing he owns the complete self-driving pipeline — multi-modal perception across LiDAR, radar, camera, IMU, GNSS and event-based sensors; robust state estimation and localisation; multi-agent motion planning in dense traffic; and control at the physical limits of grip and aerodynamic load. Validating this stack in A2RL, where cars race wheel-to-wheel at up to 300 km/h, forces a standard of safety, determinism and real-time reliability that few autonomy programmes ever encounter.

This work has produced concrete, externally benchmarked results: a P2 overall finish at the 2024 Abu Dhabi Autonomous Racing League and the world’s first autonomous overtake on a Formula 1 circuit. Beyond competition, the same capability is being productised.

A second strand of Ilya’s work concerns the observability and verification of embodied AI. Racing generates enormous volumes of multi-sensor telemetry, and the methods his team uses to capture, replay and analyse it, extending data-observability practice from software into Physical AI, lead directly to the reliability questions that govern any safety-critical autonomous system.

Robotics & autonomous machines · autonomous mobility · sensor fusion and perception · state estimation and localisation · multi-agent motion planning · vehicle control at the limit · simulation-to-real transfer · observability of embodied AI.

Autonomous driving & racing

  • T. Hansen, A. Gomez Chavez, I. Shimchik, A. Birk. Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain. CoRR abs/2604.14013 (2026).
  • A. Buyval, A. Gabdullin, R. Mustafin, I. Shimchik. Realtime Vehicle and Pedestrian Tracking for Didi-Udacity Self-Driving Car Challenge. ICRA 2018: 2064–2069.
  • I. Zubov, I. Afanasyev, A. Gabdullin, R. Mustafin, I. Shimchik. Autonomous Drifting Control in 3D Car Racing Simulator. IEEE Conf. on Intelligent Systems (IS) 2018: 235–241.
  • A. Buyval, A. Gabdullin, R. Mustafin, I. Shimchik. Deriving Overtaking Strategy from Nonlinear Model Predictive Control for a Race Car. IROS 2017: 2623–2628.

Robotics & locomotion

  • R. Khusainov, I. Shimchik, I. Afanasyev, E. Magid. Toward a Human-like Locomotion: Modelling Dynamically Stable Locomotion of an Anthropomorphic Robot in Simulink Environment. ICINCO (2) 2015: 141–148.
  • R. Khusainov, I. Shimchik, I. Afanasyev, E. Magid. 3D Modelling of Biped Robot Locomotion with Walking Primitives Approach in Simulink Environment. ICINCO Selected Papers 2015: 287–304.

Granted US patents

  • US 12,478,895 — Automatic automotive race management. (2025)
  • US 12,275,393 — System and method for optimising performance of an autonomous race car. (2025)
  • US 12,162,514 — Multi-layered approach for path planning and its execution for autonomous cars. (2024)
  • US 12,602,748 — Automatically enhancing image quality in a machine-learning training dataset using deep generative models. (2026)
  • US 12,591,913 — System and method for machine-learning-based brand advertising-rate calculation in a video. (2026)
  • US 12,579,810 — System and method for automatic events identification on video. (2026)
  • US 12,437,540 — System and method for automatic video summarization. (2025)
  • US 12,646,318 — System and method for fast adaptive brand-logo detection on video with open-set approach. (2024)

Pending US applications

  • App. 18/784,071 — Local planning for autonomous vehicles using multiple cameras. (2026)
  • App. 18/417,553 — Model-predictive path-integral controller guided by a large vision-language model for autonomous vehicle path planning. (2025)
  • App. 18/412,871 — Overtaking orchestration system for autonomous racing. (2025)
  • App. 18/533,846 — Optimal slip-angle steering control for vehicles. (2025)
  • App. 18/956,247 — Systems and methods for trajectory determination using periodic verification of vehicle control parameters. (2025)
  • App. 18/322,309 — Systems and methods for automatically identifying outliers in a machine-learning training dataset. (2024)
  • US 2026/0158911 — AI driving assistant providing personalized and emotionalized driving instructions. (pending)
Autonomous mobility

Autonomous mobility

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