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Hierarchy of Motor Control01:18

Hierarchy of Motor Control

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Related Experiment Video

Updated: Nov 18, 2025

Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze
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Multi-Task End-to-End Self-Driving Architecture for CAV Platoons.

Sebastian Huch1,2, Aybike Ongel1, Johannes Betz3

  • 1TUMCREATE, 1 CREATE Way, #10-02 CREATE Tower, Singapore 138602, Singapore.

Sensors (Basel, Switzerland)
|February 6, 2021
PubMed
Summary

This study introduces an end-to-end deep learning architecture for connected and autonomous vehicles (CAVs) platoons in urban settings. The novel approach enhances safety and efficiency by enabling vehicles to communicate and adapt driving tasks.

Keywords:
artificial neural networksconnected and autonomous vehiclesend-to-end learningmulti-task learningsimulationurban vehicle platooning

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Area of Science:

  • Robotics and Artificial Intelligence
  • Transportation Engineering
  • Computer Science

Background:

  • Connected and autonomous vehicles (CAVs) offer potential benefits like reduced emissions, improved safety, and enhanced comfort.
  • CAV platooning, with close inter-vehicle distances, promises further gains in energy efficiency, space utilization, and reduced travel times.
  • Limited research exists on self-driving algorithms specifically for urban CAV platoons.

Purpose of the Study:

  • To propose a novel end-to-end deep learning architecture for self-driving CAV platoons in urban environments.
  • To develop a multi-task model capable of controlling both leading and following vehicles within a platoon.
  • To integrate sensor data with vehicle-to-vehicle (V2V) communication for robust control.

Main Methods:

  • An end-to-end deep learning approach using a single neural network to predict steering and throttle/brake commands.
  • Input data includes front-facing camera images augmented with V2V communication information.
  • Training and testing conducted in a simulated urban environment with dynamic traffic, comparing against recurrent neural networks and transfer learning.

Main Results:

  • The proposed architecture demonstrated successful control of CAV platoons in simulation.
  • A two-vehicle platoon achieved 67% completion rate in the training environment and 40% in an unseen environment.
  • V2V communication effectively eliminated causal confusion for following vehicles, a common issue in end-to-end systems.

Conclusions:

  • The developed end-to-end architecture is a viable solution for urban CAV platooning.
  • The integration of V2V communication significantly enhances the reliability and safety of autonomous platooning.
  • This research paves the way for more efficient and safer urban transportation systems.