Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Kolekar, Suresh Sudam; Gite, Shilpa; Pradhan, Biswajeet; Kotecha, Ketan

doi:10.1109/access.2021.3116303

Cited by 13 publications

(5 citation statements)

References 112 publications

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“…There have been numerous examples of research performed to model and predict behavior in a driving scenario [53]. These topics include methods which model pedestrian behavior [20] or the behavior of other drivers [13,18,31].…”

Section: Driver Prediction and Autonomous Drivingmentioning

confidence: 99%

Modeling Human Behavior Part I -- Learning and Belief Approaches

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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Section: Driver Prediction and Autonomous Drivingmentioning

confidence: 99%

Modeling Human Behavior Part I -- Learning and Belief Approaches

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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“…There have been numerous examples of research performed to model and predict behavior in a driving scenario [60].…”

Section: Driver Prediction and Autonomous Drivingmentioning

confidence: 99%

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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As we discussed in Part I of this topic [30], there is a clear desire to model and comprehend human behavior. Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. In Part I, we discussed learning methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior as well as topics relating to the use of or accounting for beliefs with respect to applicable skills or mental states of others. In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning. We will arrange these topics as follows (i) methods such as cognitive architectures, cognitive heuristics, and related which demonstrate assumptions of limitations on cognitive resources and how that impacts decisions and (ii) methods which generate and utilize representations of bias or uncertainty to model human decision-making or the future outcomes of decisions.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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“…There already exists a couple of survey papers on pedestrian trajectory prediction. Models for both pedestrian and vehicle trajectory prediction are reviewed in [7], [8] while showing the similarities between the motion models used for vehicles and pedestrians [7]. But the authors have not discussed the differences between the various agent classes and their interactions caused by their different dynamic nature which is one of the focuses of our work.…”

Section: Introductionmentioning

confidence: 99%

“…But the authors have not discussed the differences between the various agent classes and their interactions caused by their different dynamic nature which is one of the focuses of our work. Kolekar et al [8] pointed out the importance of using the traffic rules and road geometry as input for a promising behaviour prediction model since they were focused on road scenarios, whereas our work is focused on reviewing the existing models that can be applied to unstructured environments (i.e., those without assumptions on traffic rules). In addition, not all the papers surveyed above necessarily have both types of agents simultaneously to consider pedestrian-vehicle interaction.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Golchoubian

Ghafurian

Dautenhahn

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle's interactions with the pedestrians on pedestrians' future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.

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Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Cited by 13 publications

References 112 publications

Modeling Human Behavior Part I -- Learning and Belief Approaches

Modeling Human Behavior Part I -- Learning and Belief Approaches

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

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