Control of a commercial vehicle by a tetraplegic human using a bimanual brain-computer interface

• URL: http://arxiv.org/abs/2508.11805v1
• Date: Fri, 15 Aug 2025 21:24:34 GMT
• Title: Control of a commercial vehicle by a tetraplegic human using a bimanual brain-computer interface
• Authors: Xinyun Zou, Jorge Gamez, Meghna Menon, Phillip Ring, Chadwick Boulay, Likhith Chitneni, Jackson Brennecke, Shana R. Melby, Gracy Kureel, Kelsie Pejsa, Emily R. Rosario, Ausaf A. Bari, Aniruddh Ravindran, Tyson Aflalo, Spencer S. Kellis, Dimitar Filev, Florian Solzbacher, Richard A. Andersen,
• Abstract summary: Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution.<n>We developed a bimanual BCI system to drive a vehicle in both simulated and real-world environments.
• Score: 0.4713176035149453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution. However, the implementation of this technology has been largely limited to laboratory settings, with few real-world applications. We developed a bimanual BCI system to drive a vehicle in both simulated and real-world environments. We demonstrate that an individual with tetraplegia, implanted with intracortical BCI electrodes in the posterior parietal cortex (PPC) and the hand knob region of the motor cortex (MC), reacts at least as fast and precisely as motor intact participants, and drives a simulated vehicle as proficiently as the same control group. This BCI participant, living in California, could also remotely drive a Ford Mustang Mach-E vehicle in Michigan. Our first teledriving task relied on cursor control for speed and steering in a closed urban test facility. However, the final BCI system added click control for full-stop braking and thus enabled bimanual cursor-and-click control for both simulated driving through a virtual town with traffic and teledriving through an obstacle course without traffic in the real world. We also demonstrate the safety and feasibility of BCI-controlled driving. This first-of-its-kind implantable BCI application not only highlights the versatility and innovative potentials of BCIs but also illuminates the promising future for the development of life-changing solutions to restore independence to those who suffer catastrophic neurological injury.

Related papers

• Driver-Intention Prediction with Deep Learning: Real-Time Brain-to-Vehicle Communication [2.7998963147546143]
  This study presents a novel method for predicting a driver's intention to steer using electroencephalography (EEG) signals through deep learning.<n>A driving simulator created a controlled environment in which participants imagined controlling a vehicle during various driving scenarios.<n>A convolutional neural network (CNN) classified the detected EEG data with minimal pre-processing.
  arXiv  Detail & Related papers  (2026-01-08T16:29:08Z)
• A Systematic Study of Multi-Agent Deep Reinforcement Learning for Safe and Robust Autonomous Highway Ramp Entry [0.0]
  We study a highway ramp function that controls the vehicles forward-moving actions to minimize collisions with the stream of highway traffic into which a merging (ego) vehicle enters.<n>We take a game-theoretic multi-agent (MA) approach to this problem and study the use of controllers based on deep reinforcement learning (DRL)<n>The work presented in this paper extends existing work by studying the interaction of more than two vehicles (agents) and does so by systematically expanding the road scene with additional traffic and ego vehicles.
  arXiv  Detail & Related papers  (2024-11-21T21:23:46Z)
• FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing [71.76084256567599]
  We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL) Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
  arXiv  Detail & Related papers  (2023-04-19T17:33:47Z)
• Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data [56.49494318285391]
  We present Sim2Seg, a re-imagining of RCAN that crosses the visual reality gap for off-road autonomous driving. This is done by learning to translate randomized simulation images into simulated segmentation and depth maps. This allows us to train an end-to-end RL policy in simulation, and directly deploy in the real-world.
  arXiv  Detail & Related papers  (2022-10-25T17:50:36Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits [81.22616193933021]
  The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021. It will benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. It is an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations.
  arXiv  Detail & Related papers  (2022-02-08T11:55:05Z)
• A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge [49.976633450740145]
  This paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge.
  arXiv  Detail & Related papers  (2020-10-23T18:07:48Z)
• BCI-Controlled Hands-Free Wheelchair Navigation with Obstacle Avoidance [0.5801044612920815]
  Brain-Computer interfaces (BCI) are widely used in reading brain signals and converting them into real-world motion. This paper looks specifically towards combining the BCI's latest technology with ultrasonic sensors to provide a hands-free wheelchair that can efficiently navigate through crowded environments.
  arXiv  Detail & Related papers  (2020-05-08T17:56:11Z)
• Intelligent Roundabout Insertion using Deep Reinforcement Learning [68.8204255655161]
  We present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver.
  arXiv  Detail & Related papers  (2020-01-03T11:16:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.