A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Leon, Florin; Gavrilescu, Marius

doi:10.3390/math9060660

Cited by 106 publications

(36 citation statements)

References 144 publications

(258 reference statements)

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“…Qiao et al [14] abstracted trajectory as a series of discrete motions and used Hidden Markov Model (HMM) to predict moving objects' trajectories. Moreover, heuristic-based classifiers [15], random forest classifiers [16], and RNNs have been adopted for maneuver recognition. These methods are more advanced and reliable, but they still regard vehicles as independent entities and ignore vehicles' impact.…”

Section: Literature Reviewmentioning

confidence: 99%

A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction

Jian

Zhou

Wang

et al. 2021

IJGI

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Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. The ignorance of inter-vehicle interaction and environment influence degrades these models in real-world datasets. To address this issue, we propose a novel Dynamic and Static Context-aware Attention Network named DSCAN in this paper. The DSCAN utilizes an attention mechanism to dynamically decide which surrounding vehicles are more important at the moment. We also equip the DSCAN with a constraint network to consider the static environment information. We conducted a series of experiments on a real-world dataset, and the experimental results demonstrated the effectiveness of our model. Moreover, the present study suggests that the attention mechanism and static constraints enhance the prediction results.

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Section: Literature Reviewmentioning

confidence: 99%

A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction

Jian

Zhou

Wang

et al. 2021

IJGI

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“…However, CNN are not widely used to predict pedestrian trajectories, because these are non-sequential methods, which makes it difficult to design the network input and output [174]. They are more used for trajectory predictions of road vehicles [208] or the prediction of pedestrian behaviors for autonomous vehicles [209], [210]. The first CNN designed to model and predict pedestrian trajectories is the "Behavior-CNN" from Yi et al [174].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

“…In turn, the discriminator takes the generated samples and the training data and tries to distinguish whether a given sample belongs to the true data distribution or is generated by the generator. Both components are engaged in a competition similar to a two-player min-max game where each one tries to outsmart the other one [40], [208]. From this process, the generator learns to generate data that resemble the true data distribution.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

“…In these cases the KB approach can not take as much use of the data as the DL approach can. In general, the KB approach under-performs the DL approach in case of large amount of data [208], [4]. Furthermore, the KB approach only simulates average pedestrian behavior, because it is difficult to capture the complete crowd behavior range with a single model [97].…”

Section: B Application Oriented Comparisonmentioning

confidence: 99%

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Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches

Korbmacher¹,

Tordeux²

2021

Preprint

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In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task depending on many external factors. The topology of the scene and the interactions between the pedestrians are just some of them. Due to advancements in data-science and data collection technologies deep learning methods have recently become a research hotspot in numerous domains. Therefore, it is not surprising that more and more researchers apply these methods to predict trajectories of pedestrians. This paper compares these relatively new deep learning algorithms with classical knowledge-based models that are widely used to simulate pedestrian dynamics. It provides a comprehensive literature review of both approaches, explores technical and application oriented differences, and addresses open questions as well as future development directions. Our investigations point out that the pertinence of knowledge-based models to predict local trajectories is nowadays questionable because of the high accuracy of the deep learning algorithms. Nevertheless, the ability of deep-learning algorithms for large-scale simulation and the description of collective dynamics remains to be demonstrated. Furthermore, the comparison shows that the combination of both approaches (the hybrid approach) seems to be promising to overcome disadvantages like the missing explainability of the deep learning approach.

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“…Since the middle of the 1980s, several universities, research centers, and automobile manufacturers have researched and built intelligent vehicles. Furthermore, for efficient and secure navigation on the road with mixed traffic actors, the intelligent vehicle should understand the current state of surrounding traffic actors and predict their future behavior [1].…”

Section: Introductionmentioning

confidence: 99%

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

Kolekar

Gite²,

Pradhan

et al. 2021

IEEE Access

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Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques.Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation. INDEX TERMS intelligent driving; deep learning; intelligent vehicles; vehicle behavior prediction; pedestrian behavior prediction. Inclusion criteriaResearch articles must be original research articles instead of review/survey articles. Research articles that are published between2011 and 2021.Research articles that answer at least one research question. Research articles must contain search keywords either in the title, abstract, or full text. The proposed solution in the research article must be evaluated. The solution developed must be aimed to solve problems on behavior prediction of traffic actors. Exclusion criteriaNon-English Research articles Duplicate research articles Research articles with non-availability of full text Research articles that are not relevant to pedestrians and vehicle behavior prediction concerning intelligent vehicle III. RESULTSThis section summarizes the findings of systematic analysis. It answers the research questions posed above based on the results of this review process.

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A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Cited by 106 publications

References 144 publications

A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction

A Dynamic and Static Context-Aware Attention Network for Trajectory Prediction

Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches

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

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