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Found 8 papers, 5 papers with code
A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field
Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning.
Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios
In addition, we study the impact of the proposed prediction approach on motion planning and control tasks using extensive merging scenarios from the exiD dataset.
Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks
Due to the uncertain future behaviour of vehicles, multiple future behaviour modes are often plausible for a vehicle in a given driving scene.
Prediction Based Decision Making for Autonomous Highway Driving
This paper proposes a Prediction-based Deep Reinforcement Learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving.
Fast and Robust Registration of Partially Overlapping Point Clouds
The proposed method achieves on-par performance with state-of-the-art methods on the KITTI dataset, and outperforms existing methods for low overlapping point clouds.
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks
Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records.
Visual Sensor Pose Optimisation Using Visibility Models for Smart Cities
Visual sensor networks are used for monitoring traffic in large cities and are promised to support automated driving in complex road segments.
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review
Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behaviour prediction in this paper.
