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

arXiv: https://arxiv.org/abs/2202.01645 · HTML: https://mlsanity.com/paper/2202.01645/

## Key facts

- Cited by 1 papers in the map (data through 14 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.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 3 of 3)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence](https://mlsanity.com/paper/2107.06543/) (2021)

## Built on by (most cited first, 1 of 1)

- [Federated Adaptation of Reservoirs via Intrinsic Plasticity](https://mlsanity.com/paper/2206.11087/) (2022)

## 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.