Automated Transport Systems
Automated Transport Systems
Automated Transport Systems
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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.
Sub-projects
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 energy efficient 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 will focus on addressing this tradeoff by
looking beyond the standard MPC techniques of linearization. The project will heavily rely on applied
non-linear programming and direct and indirect optimal control.
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.
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Interacting Motion Control
Pavel Anistratov (academic PhD student), Linkping University
This PhD project will consider interacting motion control by means of trajectory planning and
optimization. It will take maneuvering capabilities of the vehicle, other vehicles, and traffic situation
into account in order to formulate objective functions, constraints and choose vehicle models.