mlsanityData to 7 Oct 2026

AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

arXiv 2202.01645 ↗First posted 3 Feb 2022cs.AI cs.HCcited by 1

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving simulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 3 of its 12 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

3 of 12 references.

References · 3

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2018Learning to Drive in a Daycited by 266
  3. 2021TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligencecited by 4

Built on by

Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022Federated Adaptation of Reservoirs via Intrinsic Plasticitycited by 0

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