My work on Autonomous Bus Driving has been published in the IEEE Vehicular Technology Magazine.

We implement an on-road motion planner on a Scania prototype autonomous bus. We describe the practical considerations and implementation details required for successful real-life experiments. Extensive experimental results in a test track show the method’s suitability for real-life deployment.

Paper available here: https://ieeexplore.ieee.org/document/9470918

The challenges of road driving impact both buses as well as tractor-trailer vehicles. In the case of buses, the long wheelbase introduces a conflict between centering the rear axle vehicle or centering the front axle. The same conflict arises in the tractor-trailer case as a trade-off between centering the tractor or centering the trailer. We present a framework to design optimization objectives that optimally trade-off between these conflicting objectives. Results show that the proposed design strategy produce planned paths that considerably improve the behavior of both buses and tractor-trailer vehicles by keeping the whole vehicle body in the center of the lane.

Paper available here: https://ieeexplore.ieee.org/document/9304767

I presented my work "Path Planning for Autonomous Bus Driving in Highly Constrained Environments" at the Intelligent Transportation Systems Conference 2019.

The work deals with planning motions for an autonomous bus driving in urban environments. Unlike passenger vehicles and trucks, buses have a special chassis configuration characterized by large overhangs. To increase the maneuverability of buses, expert drivers will often allow the overhangs to sweep over curbs.

In this work, we mimic the expert bus driver behavior, by explicitly taking into account the overhangs and allowing them to sweep over curbs.

Video presentation available here: https://www.youtube.com/watch?v=RT_0FVn1oDg&t=6117s

arXiv paper available here: https://arxiv.org/abs/1905.01683

I traveled to China to present my publication "Combining Lattice-Based Planning and Path Optimization in Autonomous Heavy Duty Vehicle Applications" in the Intelligent Vehicles Symposium 2018.

Lattice planners are a commonly used tool to plan motions for autonomous vehicles, however they suffer from sub-optimality and oscillatory behavior. In this work, a new optimization method for reducing these unwanted effects, is proposed, which unlike previous approaches, is implemented in a holistic fashion. The planning and optimization are done in interleaved steps, allowing the planner to benefit from the optimization procedure.

I have published an article in the European Control Conference 2018, titled "Trajectory Generation Using Sharpness Continuous Dubins-Like Paths with Applications in Control of Heavy-Duty Vehicles".

This work adapts Dubins paths, a well know concept in robotics, for planning shortest paths between two points, to heavy duty vehicles. Dubins paths are characterized by a discontinuous curvature profile, which requires an infinite steering effort, in order to be followed exactly by a vehicle. Dubins paths are modified so that they comply with the steering actuator limitations, making them easier to perform by autonomous vehicles.

In 2016, I started at Scania as an Industrial PhD, supervised by Marcello Cirillo, and KTH professors Bo Wahlberg and Jonas Mårtensson.

My research will focus on motion planning algorithms for heavy duty vehicles. Compared to passenger vehicles, trucks and buses face additional challenges when driving on roads, and as such, require additional efforts in order to become autonomous.

I took part in the Grand Cooperative Driving Challenge 2016. The goal of this challenge is to showcase autonomous and connected driving functionality, by performing complex maneuvers, such as platoon merging, intersection crossing and emergency vehicle passages.

Together with my colleagues from the Integrated Transport Research Lab, we developed the required functionalities for this challenge, both for a Scania truck, and for a prototype vehicle, RCV-Research Concept Vehicle.

In 2015 I developed SML School, a 3-day program, in which middle- and high-schoolers can try out the life of an Engineer. Through practical and hands-on experiments the students are introduced to programming, electronics and automatic control algorithms.

The common topic between all experiments is autonomous and intelligent traffic (see video).

In the summer of 2015, the Smart Mobility Lab organized showcased the benefits of automated and connected driving to His Majesty The King of Sweden. The demonstration, that can be seen in the video, explains the benefits and challenges of Platooning, a technology which promises increase fuel consumption for vehicles driving close together.

You can read more about the visit here: https://www.kth.se/is/dcs/research/control-of-transport/smart-mobility-lab/projects/miscellaneous/king-carl-xvi-gustaf-demo-1.588058

I have been involved in iQMatic, a project between Scania, Saab, Autoliv, Linköping University, and KTH Royal Institute of Technology . iQMatic set out the goal to develop a fully autonomous truck, together with a fleet and command center, for mining applications.

At KTH, together with my colleagues João Pedro Alvito and Matteo Vanin, I developed an in-house demonstration of the iQMatic project. The demonstration involves several components, such as autonomous driving functionality and fleet management capabilities (see video).

In the spring of 2015, I supervised Amro Elhassan's master thesis, which developed an autonomous driving functionality for reversing an articulated vehicle. The work was implemented in a scaled model truck at the Smart Mobility Lab (see video).

In the spring of 2014, I did my master thesis, which studied motion planning and control algorithms for autonomous vehicles. The algorithms implemented were testes in scaled model trucks at the Smart Mobility Lab (see video).

You can find the thesis here: http://www.diva-portal.org/smash/get/diva2:746355/FULLTEXT01.pdf