SmArt seLf-driving vehIcle (SALI)

Project manager
Edith Zavala
2018-05-01 - 2018-10-31

Project A-0034 within SAFER's Open Research at AstaZero Program.

The SALI project aims at implementing and testing an open software engineering solution to integrate self-* capabilities such self-healing, self-optimization and self-improvement to self-driving vehicles for responding to runtime challenging factors such as unpredictability, faults and limited resources.
Moreover, it aims at documenting the ongoing collaboration between the vehicle laboratory “ChalmersRevere” (Sweden) and the university “Universitat Politècnica de Catalunya” (Spain).

The SALI project will provide advancements in the research areas of autonomous vehicles and software engineering. The major novel contribution will be in exploiting thousands of heterogeneous runtime data such sensor data and v2x communications, for providing smarter self-* capabilities to self-driving vehicles. The results of this project will be published in the form of scientific contributions (e.g., an article in an indexed journal), making them available to the great research community interested on this topic.

The implementation of the SALI project solution will be performed in the context of the “ChalmersRevere” laboratory. That is, we will use the resources offered by the laboratory, for designing, developing and testing software solutions for automotive systems, in order to develop the SALI project solution.
Concretely, we will use the OpenDLV framework for building a software solution to be tested in a simulation, miniature vehicles and finally the AstaZero environment.
The three main runtime challenging factors that affect self-driving vehicles and that motivate the SALI project are: unpredictability, faults and limited resources. Thus, we have designed three main use cases for testing our solution, one per runtime factor. The first use case (uc1) consists on testing the ability of SALI self-driving vehicles to respond to an unpredictable event such witnessing a road accident e.g., a crash. The second use case (uc2) tests the ability of SALI self-driving vehicles to respond to (indispensable and not) sensors failures. Finally, the third use case (uc3) focuses on testing how SALI selfdriving vehicles manage their resources for satisfying requirements and constraints, e.g., limited battery in electric vehicles.

Short facts

Project title: SmArt seLf-driving vehIcle (SALI)
Project type:
Research area:
Systems for accident prevention and AD

Project publications

Current Self-Adaptive Systems (SASs) such as Autonomous Vehicles (AVs) are systems able to deal with highly complex contexts. However, due to the use of static feedback loops they are not able to respond to unanticipated situations
Edith Zavala, Xavier Franch, Jordi Marco & Christian Berger
Published in
Enterprise Information Systems
Year of publication
Most of the current self-adaptive systems (SASs) rely on static feedback loops such as the IBM’s MAPE-K loop for managing their adaptation process. Static loops do not allow SASs to react to runtime events such as
Edith Zavala, Xavier Franch, Jordi Marco, Christian Berger
Published in
Future Generation Computer Systems
Year of publication
Nowadays, most of the approaches supporting self-adaptive systems (SASs) rely on static feedback control loops, for managing their adaptation process. One of the most popular feedback loops is the MAPE-K loop. In this loop, the Monitor
Edith Zavala
Published in
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
Year of publication
The SALI project aimed at investigating how challenging runtime factors such as unpredictability, faults and limited resources affect and could be managed in self-driving vehicles (SDVs). In order to achieve that, a series of experiments have
Edith Zavala, Christian Berger, Xavier Franch, Jordi Marco
Year of publication

Safer – Vehicle and Traffic Safety Centre

SAFER is the open research arena where researchers and expertise work together to create safe mobility. Our traffic safety approach covers people, vehicles and the infrastructure – and together we contribute to safer road transports and smarter, more sustainable cities.

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