This page has only limited features, please log in for full access.

Unclaimed
Björn Åstrand
Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 301 18 Halmstad, Sweden

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 18 May 2021 in Smart Cities
Reads 0
Downloads 0

Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

ACS Style

Cristofer Englund; Eren Aksoy; Fernando Alonso-Fernandez; Martin Cooney; Sepideh Pashami; Björn Åstrand. AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities 2021, 4, 783 -802.

AMA Style

Cristofer Englund, Eren Aksoy, Fernando Alonso-Fernandez, Martin Cooney, Sepideh Pashami, Björn Åstrand. AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control. Smart Cities. 2021; 4 (2):783-802.

Chicago/Turabian Style

Cristofer Englund; Eren Aksoy; Fernando Alonso-Fernandez; Martin Cooney; Sepideh Pashami; Björn Åstrand. 2021. "AI Perspectives in Smart Cities and Communities to Enable Road Vehicle Automation and Smart Traffic Control." Smart Cities 4, no. 2: 783-802.

Journal article
Published: 02 October 2019 in Sensors
Reads 0
Downloads 0

As human drivers, we instinctively employ our understanding of other road users' behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction.

ACS Style

Naveed Muhammad; Björn Åstrand. Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving. Sensors 2019, 19, 4279 .

AMA Style

Naveed Muhammad, Björn Åstrand. Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving. Sensors. 2019; 19 (19):4279.

Chicago/Turabian Style

Naveed Muhammad; Björn Åstrand. 2019. "Predicting Agent Behaviour and State for Applications in a Roundabout-Scenario Autonomous Driving." Sensors 19, no. 19: 4279.

Journal article
Published: 14 December 2018 in Sensors
Reads 0
Downloads 0

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method.

ACS Style

Naveed Muhammad; Björn Åstrand. Intention Estimation Using Set of Reference Trajectories as Behaviour Model. Sensors 2018, 18, 4423 .

AMA Style

Naveed Muhammad, Björn Åstrand. Intention Estimation Using Set of Reference Trajectories as Behaviour Model. Sensors. 2018; 18 (12):4423.

Chicago/Turabian Style

Naveed Muhammad; Björn Åstrand. 2018. "Intention Estimation Using Set of Reference Trajectories as Behaviour Model." Sensors 18, no. 12: 4423.

Book chapter
Published: 31 December 2013 in Springer Tracts in Advanced Robotics
Reads 0
Downloads 0

The main focus of this paper is to present a case study of a SLAM solution for Automated Guided Vehicles (AGVs) operating in real-world industrial environments. The studied solution, called Gold-fish SLAM, was implemented to provide localization estimates in dynamic industrial environments, where there are static landmarks that are only rarely perceived by the AGVs. The main idea of Gold-fish SLAM is to consider the goods that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks to compute online estimates of AGV poses. The solution is tested and verified in a factory of paper using an eight ton diesel-truck retrofitted with an AGV control system running at speeds up to 3 m/s. The paper includes also a general discussion on how SLAM can be used in industrial applications with AGVs.

ACS Style

Henrik Andreasson; Abdelbaki Bouguerra; Björn Åstrand; Thorsteinn Rögnvaldsson. Gold-Fish SLAM: An Application of SLAM to Localize AGVs. Springer Tracts in Advanced Robotics 2013, 92, 585 -598.

AMA Style

Henrik Andreasson, Abdelbaki Bouguerra, Björn Åstrand, Thorsteinn Rögnvaldsson. Gold-Fish SLAM: An Application of SLAM to Localize AGVs. Springer Tracts in Advanced Robotics. 2013; 92 ():585-598.

Chicago/Turabian Style

Henrik Andreasson; Abdelbaki Bouguerra; Björn Åstrand; Thorsteinn Rögnvaldsson. 2013. "Gold-Fish SLAM: An Application of SLAM to Localize AGVs." Springer Tracts in Advanced Robotics 92, no. : 585-598.