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Cristofer Englund

Enhetschef

Tel

+46 70 856 02 27

Email

cristofer.englund@ri.se

Location

Göteborg

Unit

Människa-autonomi (102303)

ORCID

0000-0002-1043-8773

Cristofer Englund is a research manager within Humanized Autonomy and is responsible for research in automated and connected vehicles at RISE. Cristofer is also associate professor in data mining at Halmstad University. Within Humanized Autonomy are researchers with broad skills in e.g. behavioral science, philosophy, industrial design, embedded systems, business modeling, wireless communication, machine learning and image processing.

Cristofer is leading several projects within automated vehicles and is particularly interested in the interaction between different players in the transport system. Specifically about how behavior can be modeled to facilitate machines to interpret human, and other machine behavior to create safer and more efficient transport.

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Safe and explainable critical embedded systems based on AI

SAFEXPLAIN addresses the challenges of DL-based solution certification by (i) designing explainable DL solutions and (ii) designing FUSA security patterns for different DL usage levels

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…

eSPARK - Assessing electric scooters' role in the fossil-free transport system

E-scooters have populated cities and led to both positive and negative reactions and it is unclear if they have a role to play in a transport efficient society. There is, therefore, a need for acquiring better knowledge about how …

SMILE III – Safety analysis and verification/validation of ML based systems

The SMILE program develops method(s) that allow DML-based functions to be included into safety critical vehicular applications. The SMILE III project further develops the Safety Cage concept developed within SMILE I/II, into a ref…

Verification and validation of automated systems’ safety and security

The VALU3S project aims to evaluate the state-of-the-art verification and validation (V&V) methods and tools for automated systems in automotive industry, agriculture, railway, healthcare, aerospace and industrial automation a…

i-GAME - Interoperable GCDC AutoMation Experience

The i-GAME project raised the level for automated driving by combining existing advanced driver assistance systems with car-to-car communication (V2V). The 3-year FP7 program, with roots in the "Grand Cooperative Driving Chal…

Radar reflecting pavement markers for vehicle automation

Redundant and accurate vehicle positioning system is crucial for automated driving. Today there is no perfect solution yet that would meet such requirements. This project studied the possibility of adding to the vehicle positionin…

CHOReVOLUTION

CHOReVOLUTION is an EU H2020 project that provides a platform for the provision of innovative IoT applications. CHOReVOLUTION undertakes both research and innovation tasks.

Cyber Resilience for Vehicles

The introduction of autonomous and connected vehicles has brought new cybersecurity challenges to the automotive industry and put requirements on the dependability of vehicles in the presence of cyber-attacks. CyReV* focuses on cy…

Trustworthy AI: The art of getting users to dare to rely on technology

AI and machine learning can simplify and improve our everyday life in our free time, at work, and in global societal development. But how do we dare trust the machines?

These 15 questions reveal: How ready is your new data-driven project?

Data can be used to free up time and rationalise, and for improvement of processes – and even in order to generate fresh business. By evaluating your data-driven improvement projects using 15 specific issues, much frustration and …

How should autonomous vehicles give back control to drivers?

AI technology can handle the driving of autonomous vehicles on certain routes without compromising on safety – all while the driver is free to engage in relaxing activities such as reading, drawing, playing chess or even sleeping.…

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…

Enabling seamless transports through sustainable transport nodes

Seamlessness in transports is an important ingredient for sustainability. To enable seamless transports, RISE puts emphasis on empowering transport nodes to become more integrated in the transport chain.

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…

Driver modelling

A paradigm shift within the transport system is at our doorstep. Transport is becoming commodity and vehicle automation and servification are the enablers. As the role of the driver changes from actively driving, to passively ridi…

Image Analysis

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 …

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