LSTM-Based Human-Driven Vehicle Trajectory Prediction in a Connected and Autonomous Vehicle Environment

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based Long Short-Term Memory (LSTM) models for HDV trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation SIMulation (NGSIM) US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step trajectory predictions. Furthermore, the authors conduct grid-level average attention weight analysis and identify the CAVs with higher impact on the target HDV’s future trajectories.

• Record URL:
  https://annualmeeting.mytrb.org/OnlineProgram/Details/15713
• Supplemental Notes:
  • This paper was sponsored by TRB committee AED50 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Lin, Lei
  • Gong, Siyuan
  • Peeta, Srinivas
  • Wu, Xia
• Conference:
  • Transportation Research Board 100th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2021-1-5 to 2021-1-29
• Date: 2021

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 14p

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Behavior; Connected vehicles; Machine learning; Traffic flow; Vehicle mix; Vehicle to vehicle communications; Vehicle trajectories
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01764189
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-01269
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 11:00AM