Department of Mechanical Engineering

Autonomous Mobility Laboratory

The page contains introduction to what we do, our facilities, recent research and people who are in this research group
picture of AMlab

Autonomous Vehicle Operation

Our lab is at the forefront of automated driving technologies, with a focus on the challenging winter conditions. We develop autonomous vehicles adept at navigating icy, snowy, and unpredictable roads. We employ a differential robot named Dbot, outfitted with a Velodyne VLP-16 and stereo cameras, alongside a TurtleBot equipped with 2D LiDARs, for indoor mapping and localization. Our research also encompasses powertrain optimization, aiming for vehicles that are autonomous, energy-efficient, and eco-friendly. Our facilities include a spacious workshop with four vehicle bays, a dedicated battery cell testing room, a cold chamber, and an electronics lab.

Our autonomous car ALEX

Intelligent Transportation Systems

Intelligent transportation systems are pivotal in the shift toward autonomous, safe, and green mobility. Our work focuses on machine vision in vehicles and as part of intelligent infrastructure to enhance the perception of road conditions, braking events, and interior cleanliness. Our systems detect and track road users, providing warnings to drivers about hazards they may not notice.


Powertrain and Operation Optimization

Sustainability is key in vehicle design. Vehicle performance hinges on the driver, route, traffic, and weather—all of which influence powertrain configuration. These variables can lead to inefficient driving and the need for oversized powertrain components like batteries. Our research investigates these uncertainties and their effect on energy consumption to refine the design and operation of powertrains.

Current Research

    Alex friciton

    "Friction between the road and vehicle tires plays a key role in defining how a vehicle should be controlled and maneuvered in winter conditions. To enable safe automated driving in winter, our research group is developing computer vision-based methodology for estimating the friction properties of the road. This is achieved with deep learning techniques."  Ojala Risto


    ”Storing electrical energy from renewable sources onboard the ship represents a promising zero-emission pathway for coastal maritime shipping. The key technical challenge for battery-electric ships arises from the extra volume and weight of the battery system, propulsion motor, and power electronics relative to combustion engines and tanks of conventional ships. Our group is developing modeling techniques to accelerate the design space exploration of novel battery-electric ship concepts. These techniques, known as convex transformations, can accelerate solution times of design problems from hours or days to only a few seconds.” Ritari Antti


    "In the field of autonomous driving, targeted external perception is a key component in executing optimum control strategies to ensure the safety of vulnerable road users. The research focuses on developing an attention mechanism in the form of utilizing scene-flows to obtain moving road users in the neighborhood of an ego-vehicle in different driving scenarios. The work revolves on the underlying concepts of sensor integration, dynamic background refinement, pattern analysis and vision-based prediction models."   Jayawickrama Nilusha

    snow particle filtering

    "I have been a doctoral candidate in the autonomous mobility laboratory since early 2022. My research focuses on autonomous driving in adverse conditions. I specialize in solutions to mitigate weather effects such as snowfall on sensor data using deep learning tools"  Alvari Seppänen

    Amlab research 2

    "Detection of the drivable area in all driving conditions is extremely important for autonomous vehicles and advanced driver assistance systems. My research concentrates on drivable area detection in demanding winter driving conditions with deep learning methods. For better scalability, I try to find ways to automatize the learning process that usually requires human supervision." Alamikkotervo Eerik

    Amlab research 3

    Mapping in dynamic environments, produces dynamic points, adversely effecting localization. This study aims to remove the dynamic points by leveraging the observation that stationary points, over multiple scans, have a smaller convex hull volume compared to dynamic ones. Habibiroudkenar Pejman 

    sahoo research

    "Vehicular energy-efficiency plays a pivotal role in reducing the carbon footprint in the world, especially for long-haul vehicles. To achieve such energy-efficiency in hybrid vehicles, our research group is focused on developing robust controllers which decide when to use internal combustion engines and when to use electric motors fitted with electric batteries in such vehicles. The robust controllers are developed using model-based and convex optimization techniques."  Sahoo Subhadyuti

    Latest publications

    Out-of-distribution- and location-aware PointNets for real-time 3D road user detection without a GPU

    Alvari Seppänen, Eerik Alamikkotervo, Risto Ojala, Giacomo Dario, Kari Tammi 2024

    Architecture for determining the cleanliness in shared vehicles using an integrated machine vision and indoor air quality-monitoring system

    Nilusha Jayawickrama, Enric Perarnau Ollé, Jesse Pirhonen, Risto Ojala, Klaus Kivekäs, Jari Vepsäläinen, Kari Tammi 2023

    Infrastructure camera calibration with GNSS for vehicle localisation

    Risto Ojala, Jari Vepsalainen, Jesse Pirhonen, Kari Tammi 2023

    4DenoiseNet: Adverse Weather Denoising From Adjacent Point Clouds

    Alvari Seppanen, Risto Ojala, Kari Tammi 2023

    Classification of Trash and Valuables with Machine Vision in Shared Cars

    Nilusha Jayawickrama, Risto Ojala, Jesse Pirhonen, Klaus Kivekas, Kari Tammi 2022

    Motion detection and classification : ultra-fast road user detection

    Risto Ojala, Jari Vepsäläinen, Kari Tammi 2022

    Brake Light Detection Algorithm for Predictive Braking

    Jesse Pirhonen, Risto Ojala, Klaus Kivekäs, Jari Vepsäläinen, Kari Tammi 2022

    Predictive Braking With Brake Light Detection-Field Test

    Jesse Pirhonen, Risto Ojala, Klaus Kivekas, Kari Tammi 2022
    More information on our research in the Aalto research portal.
    Research portal
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