This cluster concerns optimization of the overall transport efficiency performance with focus on connected collaborative self-driving vehicles by taking advantage of new possibilities for efficient communications and computing, accurate position estimation and smart decision systems.

Vision for the Cluster

Background: Automated transport systems will revolutionize the efficiency of transportation of people and goods. The European Union’s goal is to reduce greenhouse gases by 20 percent by 2020, which means the transport efficiency has to be dramatically improved. The focus has been on more and more fuel efficient and intelligent vehicles, but at the same time automated transports need to be connected in real time to surrounding local as well as central systems, to optimize the overall transport performance. For example, road intersections are accident-prone bottlenecks in the traffic system. By developing intelligent algorithms where vehicles exchange information and decide on a safe schedule for each of them passing the intersection one can improve the traffic flow at the same time as safety is increased and fuel is saved. Another example is platooning of vehicles, where safety and fuel economy can be improved, but the distributed speed control must be implemented to guarantee safety when the distance between the vehicles is decreased, at the same time a real-time coordination system is needed to dynamically create, maintain, and dissolve platoons, taking into account historical and real-time information about the state of the infrastructure. A further example is autonomous vehicles connected to transport control and command centers, used in confined areas like harbors or mines. Here the task is to optimize the transport efficiency and reliability while at the same time taking all local and global constraints into account. To summarize, the increase of computational power is enormous both in devices and on a cloud level, and will continue. Fast communications makes it possible to connect vehicles and the infrastructure to provide enormous possibilities for data analytics and real time decision making. These advances make it possible to build even more effective and sustainable solutions for transport of people and goods.

Objective: Our research focus is on connected collaborative self-driving vehicles with supporting infrastructure. Our objective is to develop methodologies and algorithms for such systems along with theory and open source code. In particular we are interested in adaptive algorithms to assure safe, efficient and reliable solutions. The key research problems explained below concern scalability, resilience and safety for heterogonous autonomous transport systems.

Connection to the other WASP Clusters: ATS is a strategic application domain cluster with strong connection to the more thematic WASP clusters. In particular to Smart Localization Systems and Large-Scale Optimization and Control, with focus on data analytics, learning, control, and distributed optimization, and to Perception and Learning and Verification in Interactive Autonomous Systems, with focus on perception methods based on fusion of multi-modal sensory information in combination with learning.

Future Demonstrations: We have Autoliv AB, Scania AB, AB Volvo and Volvo Car Group involved in this cluster through industrial PhD students. We have the knowledge and experience from ongoing projects in self-driving cars and trucks, and cooperative driving for efficient transport solutions. We now would like to study even more complex scenarios, such as cooperative heterogeneous platoons, smart intersections and connected mobility in urban environments.

Research Challenges

Scalability: The complexity of automated transport systems of many heterogeneous vehicles and humans in urban environments is huge. One idea is to build on results from other kind of networked systems and to use a layered architecture but at the same time be able to handle the time constraints for critical real-time decision making. Local solutions involving a moderate number of vehicles, in for example a platoon, or in a crossing, interact with solutions on system level guiding traffic flow. With more efficient traffic solutions, the interaction between local control and system level increase so that the dynamics at one crossing influence the traffic flow at the neighboring crossings and the overall performance on system level. Hence, algorithms at different levels need to be co-designed, and there must be a scalability feature where solutions at lower level harmonize with higher levels when larger traffic systems are considered. We will develop algorithms and methodologies to handle a large number of coordinated and collaborative vehicles.

Resilience: How do we construct solutions that are safe, secure and can interact with humans, and at the same time very robust to failures? When we start to optimize transport flows, how do we guarantee resilience with be respect to disturbances and failures? Our objective is to develop resilient and robust transport system that maintains an accepted level of performance despite disturbances, including threats of an unexpected and malicious nature. Another question is how should control and communication be co-designed to enable automated transport in urban and highway scenarios? We believe that tailored resource allocation algorithms can make enable better resilience, performance, and robustness. The challenge lies in developing such resource allocation algorithms (both centralized and distributed) in harmony with the control algorithm.

Reliability and Latency: Autonomous vehicles must make decisions in real time based on accurate and valid information. Here accurate position information and reliable communication is required. 5G technologies can hopefully provide such information, but the achievable accuracy, latency, scalability, robustness to disturbances, hardware and signal processing requirements are still unknown. Here we need to develop performance bounds, centralized and distributed positioning methods, and corresponding planning and control algorithms. For example, in intelligent maneuvering and motion control there are several unsolved questions that mainly relate to the formulation of the problem. This is true especially for what objective function to use.

Adaptation and Autonomy: Smart systems must be able to learn and adapt from their new knowledge, its past actions and even mistakes. We envision automated transport systems consisting of learning autonomous vehicles that interact with each other, real-time critical clouds, and exploit their available data and computational capabilities. We wish to understand the basic principles according to which such cloud/edge-assisted multi-agent systems should be designed if they are to learn, adapt and act in uncertain and evolving environments.

Industrial Challenges

Research in automated transport systems, including self-driving vehicles, is pushed by society to extend capacity and at the same time improve safety, efficiency and sustainability. The target for Sweden is to have a vehicle fleet that is independent of fossil fuels in 2030, and intelligent transport systems and services is one important way to achieve this objective. The current industrial challenges involves self-driving vehicles, but also other radical new ways to improve mobility of people and transport of goods. Enablers are new technologies for e.g. computing and communication leading to safer, more efficient and sustainable transport solutions. Future cooperative automated transportation solutions have to take diverse requirement such as Safety, Heterogeneity and Complexity on a System Level into account in a structured way. Companies and transport organizations are very active in demonstrating new transport solutions based on self-driving vehicles and ICT. These mobility demonstrators form an excellent base for collaboration between industry and academy to address these system challenges.


Cluster coordinator

Bo Wahlberg  KTH,



Karl Henrik Johansson, KTH

Lars Nielsen, Linköping University

Jonas Sjöberg, Chalmers

Fredrik Tufvesson, Lund University

Henk Wymeersch, Chalmers

Jonas Fredriksson, Chalmers

Krister Wolff, Chalmers

Jonas Mårtensson, KTH

Paolo Falcone, Chalmers

Lennart Svensson, Chalmers

Martin Törngren, KTH



Control of Autonomous Vehicles in Complex Traffic with Safety Constraints

Johan Karlsson (academic PhD student), Chalmers

This PhD project concerns development of control algorithms for safe and comfortable path planning and decision taking in autonomous driving. This will be done with the use of model-based control techniques that incorporate predictive information. The project focuses on the use of MPC techniques to reach this goal. Where focus will be on new ways of formulating the direct optimal control problem to obtain more efficient algorithms. WARA-CAT is an important resource when it comes to testing whether these algorithms will function inside a real vehicle, which is the ultimate goal. Both to test if they are robust enough and if they are computationally efficient enough.

Main Supervisor: Jonas Sjöberg, Co-supervisor: Nikolce Murgovski


Communication and Positioning for Automated Transport

Mohammad Ali Nazari (academic PhD student), Chalmers

This PhD project will consider wireless communication systems in the context of cooperative driving. Currently, communication between cars is based on 802.11p, which can support limited situational awareness (by broadcasting position and velocity information). The long-term goal is to harness 5G wireless signals to provide cooperative situational awareness by sharing complete maps, and cooperative control, through distributed solving of optimal control problems.

Main supervisor: Henk Wymeersch


Intelligent Avoidance Maneuvers for Autonomous Ground Vehicles

Pavel Anistratov (academic PhD student), Linköping University

This PhD project addresses time-critical avoidance maneuvers with applications to autonomous ground vehicles. The project focuses on the development of new formulations of motion-planning optimizations, with a desire to increase vehicle safety by taking into account new control possibilities of autonomous vehicles. Methods to decrease complexity of motion-planning optimizations are also considered, with an approach to obtain a full avoidance maneuver by applying a segmentation and merging strategy. This PhD project is related to WARA-CAT by studying motion-planning and control strategies for autonomous ground vehicles. The project benefits from the arena by having access to integrated autonomous vehicles for real experiments in realistic environments, which would allow test of obstacle-avoidance strategies, once they are ready for online execution strategy.

Main supervisor: Lars Nielsen, Co-supervisors: Björn Olofsson and Jan Åslund


Link Modelling for Cooperative Transport Solutions

Christian Nelson (academic PhD student), Lund University

This PhD projects concerns link modelling for cooperative transport solutions, where we put emphasis on safety critical aspects such as latency, relative positioning, reliability and their interaction with the control system for collaborative transport solutions.

Main supervisor: Fredrik Tufvesson


Coordinated Learning and Control of Vehicles

Linnea Persson (academic PhD student), KTH Royal Institute of Technology

This PhD project concerns trajectory generation and control for systems of cooperating agents, using optimization-based methods for guaranteeing safety and enabling agents to select and update their actions in real-time to achieve common objectives. This project will benefit from the various WARA autonomous vehicles in experiments for verifying cooperative control algorithms.

Main supervisor: Bo Wahlberg, Co-supervisor: Dimos Dimarogonas


Control over Mobile Networks

Dirk van Dooren (academic PhD student), KTH Royal Institute of Technology

In this PhD project the problem of real-time control over mobile networks is considered. The main focus is on the handover of control processes without sacrificing safety or performance. This is achieved by developing control strategies that take into account the characteristics of the communication system. Additionally, control-aware handover algorithms are developed that minimize the impact on the control system. The effectiveness of these methods is evaluated in various automated transportation scenariosThis project can be showcased by an autonomous transportation scenario. By utilizing the 5G infrastructure a group of vehicles can be controlled from the mobile network. When moving between base stations a handover becomes necessary to maintain connectivity. The control and handover algorithms developed in this project can be used to safely hand over the control process.

Main supervisor: Karl Henrik Johansson, Co-supervisor: James Gross


Motion Planning for Autonomous Driving within Urban Environments

Rui Oliveira (Industrial PhD student), Scania and KTH Royal Institute of Technology

This industrial PhD project concerns planning and decision making algorithms for autonomous driving applications. The focus is on heavy duty vehicles with applications in industrial environments and highway traffic. The final goal is to develop solutions which have safety and performance guarantees. The research is linked to the WARA-CAT.

Main supervisor: Bo Wahlberg, Co-supervisor: Jonas Mårtensson, Industrial supervisor: Marcello Cirillo


Driving Automation in Complex Environments

Tommy Tram (Industrial PhD student), Zenuity and Chalmers

The goal in this industrial PhD project is to consider decision making and path planning in complex traffic situations including crossings and roundabouts. The focus is on combining artificial intelligent algorithms to make decisions that can safely and comfortably maneuver in complex traffic situations with other road users. Implementing decision algorithms for self-driving car and making experiments at Asta Zero as a part of WARA-CAT. Some algorithms that are developed in parallel will also be implemented in the XC90.

Main supervisor: Jonas Sjöberg, Industrial supervisor: Mohammad Ali


Driving Automation in Complex Environments

Ivo Batkovic (Industrial PhD student), Zenuity and Chalmers
The objective of this industrial PhD project is to consider decision making and path planning in complex traffic situations including crossings and roundabouts. The research focus is to combine prediction methods of other road users with vehicle control algorithms to have safe and efficient urban driving. The research is tightly connected with the REVERE lab at Chalmers and WARA-CAT. Through the COPPLAR project, real-life and real-time demos at AstaZero have been performed.

Main supervisor: Paolo Falcone, Industrial supervisor: Mohammad Ali


Tactical Decision-Making in Dynamic Uncertain Traffic Situations

Carl-Johan Hoel (Industrial PhD Student), AB Volvo and Chalmers

This PhD project targets the problem of making autonomous, tactical decisions in uncertain traffic situations, balancing safety and efficiency. A few of the research questions are (i) How to measure quality of decision making? How can it be formulated mathematically? (ii) How can machine learning be used, integrating traffic rules, human driving behaviour and safety? (iii) How can a decision making system guarantee safety?

Main supervisor: Krister Wolff, Industrial supervisor: Leo Laine


Control of Self-driving Heavy-duty Vehicles Driving on Rough Conditions

Gonçalo Collares Pereira (industrial PhD student), Scania CV AB and KTH Royal Institute of Technology

This industrial PhD project focuses on studying the motion controller of an autonomous heavy-duty vehicle subject to disturbances. These disturbances can be external, for example, holes on the road, sand, gravel, water, ice, snow, etc., or internal disturbances, for example, actuators not behaving as expected, loss of performance of the motor, etc. The objective of the motion controller is to make the vehicle perform a given path/trajectory. The project will investigate robust controllers, taking in consideration the disturbances, in order to guarantee safety and improve the overall system performance. The demos are done at Scania.

Main supervisor: Jonas Mårtensson, Co-supervisor Bo Wahlberg, Industrial Supervisor Henrik Pettersson


Distributed Control of Heavy-Duty Vehicle Platoon Maneuvers in Traffic

Mladen Cicic (academic PhD student), KTH Royal Institute of Technology

This PhD project concerns modeling and controlling the interaction and mutual influence between different classes of vehicles in traffic. Different classifications of vehicles based on their particular behavior will be addressed, such as autonomous and human-driven vehicles, heavy-duty vehicles and passenger cars, platoons and individual vehicles, etc. The goal is to understand how we can influence the overall traffic by using a subset of vehicles that we can control directly

Main supervisor: Karl Henrik Johansson


Network Control of Autonomous Vehicles

Masoud Bahraini (academic PhD student), Chalmers

This PhD project concerns network control of autonomous vehicles with focus on problem formulation for supervisory control in case of communication limitation.

Main Supervisor: Paolo Falcone, Co-supervisor: Henk Wymeersch


Verification Methods for Automated Vehicles

Arian Ranjbar (industrial PhD student), Zenuity and Chalmers

The goal of this industrial PhD project is to explore and develop verification methods for automated vehicles. In particular how to verify and safely employ machine learning algorithms. In order to develop safe autonomous driving robust verification methods demonstrations at WARA-CAT are necessary.

Main supervisor: Jonas Fredriksson, Industrial Supervisor: Nasser Mohammadiha


Safety-Critical Traffic Situations

Viktor Fors (academic PhD student), Linköping University

The goal of this PhD project is to obtain techniques to handle safety-critical traffic situations with complicated nonlinear dynamics and significant model uncertainty to be solved in time-critical situations. This PhD project benefits from resources available at WARA-CAT. Specifically the availability of a test vehicle fitted for autonomous driving that can be used to test experimental active-safety features

Main supervisor: Lars Nielsen, Co-supervisors: Björn Olofsson and Jan Åslund


Architecting Safe Automated Driving with Legacy Platforms

Naveen Mohan (industrial PhD student), Scania and KTH Royal Institute of Technology

This industrial PhD project considers architectures for vehicular platforms for the introduction of automated driving. Primarily focusing on industrial best practices and safety standards such as ISO 26262, the aim of the project is to assure that certification concerns for safety can be met across highly variable vehicular platforms, in a cost effective manner. In particular, this project focusses on architecting methods and reference architectures to ensure compliance with ISO 26262.The long term aim is to do a safety analysis on the RCV to see if the methods used in the industry can be used in prototyping as well

Main supervisor: Professor Martin Törngren, Co-supervisor Sagar Behere, Industrial supervisors: Per Roos, Dr. Johan Svahn


Motion Planning  for Safe Stop Maneuver

Lars Svensson (academic PhD student), KTH Royal Institute of Technology

This research project aims to investigate the properties of the specific class of motion planning problems associated with a safe stop maneuver. The term safe stop describes a maneuver that takes the car from an arbitrary initial state in a road network, to the safest reachable stopping location, if a fault or hazardous event occurs. Depending on the event, the maneuver may be urgent or less urgent, may have to consider altered vehicle dynamics, or limited sensing capabilities. In addition to being of academic interest, the safe stop problem is relevant to industry, since safe stop capability is required by recent automotive design guidelines. The Research Concept Vehicle of the Integrated Transport Research Lab and WARA-CAT is used for evaluating and validating the research results

Main supervisor: Professor Martin Törngren, Co-supervisors: Lei Feng and Anna Pernestål Brenden.


Situational Assessment for Automated Drive

Samuel Scheidegger (industrial PhD student), Zenuity and Chalmers

This PhD project concerns situational assessment for autonomous driving applications. The focus is on using data from on board sensors, like cameras, lidars or radars, to build up a model of the surroundings of the vehicle. This research is connected to WARA-CAT through the COPPLAR project.

Main supervisor: Lennart Svensson, Industrial supervisor: Benny Nilsson