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Martin Torstensson

Forskare

Tel

+46 10 228 40 18

Email

martin.torstensson@ri.se

Location

Göteborg

Unit

Människa-autonomi (102303)

Ask me about

Ask me about

Digital Decision Support for Refurbishment (2DSR)

Decision-making in the circular economy can be challenging due to reverse logistics and lack of readily available data. The 2DSR project will build a decision support system (DSS) for actors in the furniture sector. The machine le…

EVOLVE

EVOLVE explores practical challenges, develops technology and processes to integrate heterogenous autonomous transportation modalities into mission-based joint operations with regards to mixed passenger/goods transportation.

Chemical Safety to Support Circular Economy (CHESS)

Sweden’s goal is to become climate-neutral by 2045. This requires a transformation of the whole society, and challenges must be tackled urgently. The transition from a linear to a circular economy (CE) carries such a transformatio…

Testing safety of intelligent connected vehicles in open mixed road environment

ICV-SAFE is a Sweden – China bilateral project with focus on investigating the safety aspects of intelligent connected vehicles (ICV) in mixed traffic environments. The project aims at taking advantage of the rapid development env…

DRAMA-2 DRiver and passenger Activity MApping simulator

DRAMA-2 builds upon the experiences obtained from the DRAMA project on the application of Machine Learning (ML) algorithms for monitoring the behavior of vehicle occupants.

RE-ENGAGE – Human activity detection to re-engage drivers in autonomous cars

The RE-ENGAGE project is a cross-disciplinary project that combines user experience (UX) and machine learning (ML). The aim of the project is to investigate, demonstrate and implement solutions that re-engage the driver in an auto…

MIDAS - anonyMIsing DAta collection for traffic Safety

MIDAS aims to solve the problem of anonymity regarding video data collected in real traffic environments. MIDAS develops machine learning algorithms to replace sensitive information in images, so that they can be saved for future …

Computer vision

Most autonomous systems rely on images to understand complex environments and make decisions. As part of the RISE digitalization, we carry out image analysis studies that utilize both traditional and AI methods to solve practical …

Explainable AI

The ability to interpret decisions made by machine learning algorithms helps ensure important criteria such as safety, fairness, unbiasedness, privacy, and reliability by allowing humans to confirm that the algorithms adhere to re…

Behaviour Modelling

A paradigm shift within the transport system is here. Historically the human driver has been responsible for both the car and the driving. As machines are introduced, they will take over the responsibility of interpreting the sur…