Interactive Trajectory Prediction of Surrounding Road Users for Autonomous Driving Using Structural-LSTM Network

Hou, Lian; Long, Xin; Li, Shengbo Eben; Cheng, Bo; Wang, Wenjun

doi:10.1109/tits.2019.2942089

Cited by 109 publications

(40 citation statements)

References 35 publications

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“…Then, they extract the interactions between agents by sharing the hidden states between all the LSTMs corresponding to a set of neighboring pedestrians. Hou et al [33] use a structural-LSTM network to learn high-level dependencies between vehicles. Similar to social LSTM, they attribute one LSTM for each vehicle.…”

Section: B Deep Learning Pattern-based Motion Predictionmentioning

confidence: 99%

Attention Based Vehicle Trajectory Prediction

Messaoud

Yahiaoui

Verroust-Blondet

et al. 2021

IEEE Trans. Intell. Veh.

199

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Self-driving vehicles need to continuously analyse the driving scene, understand the behavior of other road users and predict their future trajectories in order to plan a safe motion and reduce their reaction time. Motivated by this idea, this paper addresses the problem of vehicle trajectory prediction over an extended horizon. On highways, human drivers continuously adapt their speed and paths according to the behavior of their neighboring vehicles. Therefore, vehicles' trajectories are very correlated and considering vehicle interactions makes motion prediction possible even before the start of a clear maneuver pattern. To this end, we introduce and analyze trajectory prediction methods based on how they model the vehicles interactions. Inspired by human reasoning, we use an attention mechanism that explicitly highlights the importance of neighboring vehicles with respect to their future states. We go beyond pairwise vehicle interactions and model higher order interactions. Moreover, the existence of different goals and driving behaviors induces multiple potential futures. We exploit a combination of global and partial attention paid to surrounding vehicles to generate different possible trajectory. Experiments on highway datasets show that the proposed model outperforms the state-of-the-art performances.

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Section: B Deep Learning Pattern-based Motion Predictionmentioning

confidence: 99%

Attention Based Vehicle Trajectory Prediction

Messaoud

Yahiaoui

Verroust-Blondet

et al. 2021

IEEE Trans. Intell. Veh.

199

View full text Add to dashboard Cite

show abstract

“…The framework in [26] classifies vehicles into various categories by first detecting vehicles with YOLOv3, then extracts motion with another CNN. As a general video action prediction model, long short-term memory (LSTM) is used for target vehicle trajectory prediction [27]. However, LSTM models require more hardware resources than traditional CNNs, which may degrade the real-time performance.…”

Section: Models Motion Information Through Tracking the Corner Features For Vehicle Detection With Hidden Markowmentioning

confidence: 99%

Motion-aware vehicle detection in driving videos

KILIÇARSLAN,

TEMEL

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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This paper focuses on vehicle detection based on motion features in driving videos. Long-term motion information can assist in driving scenarios since driving is a complicated and dynamic process. The proposed method is a deep learning based model which processes Motion Frame Image. This image merges both spatial (frame) and temporal (motion) information. Hence, the model jointly detects vehicles and their motion from a single image. The trained model on Toyota Motor Europe Motorway Dataset reaches 83% mean average precision (mAP). Our experiments demonstrate that the proposed method has a higher mAP than a tracking-based model. The proposed method runs real-time in driving videos which enables the model to be used in time-critical applications such as autonomous driving and advanced driving assistance systems.

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“…In drivable and nondrivable regions, LSTM encoder-decoder architecture is proposed that uses Map-Mask patches to render trajectory predictions for different groups of traffic agents. Furthermore, a hierarchical multi-sequence learning network is used to predict long-term interactive trajectories for surrounding vehicles using a structural-LSTM (long short-term memory) network [59]. For each interacting vehicle, Structural-LSTM first assigns one LSTM.…”

Section: Recurrent Neural Network (Rnn) Modelmentioning

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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Interactive Trajectory Prediction of Surrounding Road Users for Autonomous Driving Using Structural-LSTM Network

Cited by 109 publications

References 35 publications

Attention Based Vehicle Trajectory Prediction

Attention Based Vehicle Trajectory Prediction

Motion-aware vehicle detection in driving videos

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

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