mlsanityData to 14 Oct 2026

An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems

arXiv 2308.01369 ↗First posted 2 Aug 2023cs.RO cs.AIcited by 0

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Abstract (arXiv)

Navigating automated driving systems (ADSs) through complex driving environments is difficult. Predicting the driving behavior of surrounding human-driven vehicles (HDVs) is a critical component of an ADS. This paper proposes an enhanced motion-planning approach for an ADS in a highway-merging scenario. The proposed enhanced approach utilizes the results of two aspects: the driving behavior and long-term trajectory of surrounding HDVs, which are coupled using a hierarchical model that is used for the motion planning of an ADS to improve driving safety.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 60 references that are in the map. First posted on arXiv 2 Aug 2023.

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10 of 60 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Multimodal Motion Prediction with Stacked Transformerscited by 208
  3. 2020Transformer Networks for Trajectory Forecastingcited by 181
  4. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  5. 2019Multi-Agent Tensor Fusion for Contextual Trajectory Predictioncited by 131
  6. 2018Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMscited by 77
  7. 2017Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Networkcited by 56
  8. 2018Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networkscited by 29
  9. 2019Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Drivingcited by 12
  10. 2019Interaction-aware Kalman Neural Networks for Trajectory Predictioncited by 9

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