mlsanityData to 7 Oct 2026

Exploring the stimulative effect on following drivers in a consecutive lane-change using microscopic vehicle trajectory data

arXiv 2205.11252 ↗First posted 18 May 2022cs.LG stat.APcited by 2

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Abstract (arXiv)

Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effect on following drivers in a consecutive lane-changing scenario. The microscopic trajectory data from the dataset are used for driving behavior analysis.Two discretionary lane-changing vehicle groups constitute a consecutive lane-changing scenario, and not only distance- and speed-related factors but also driving behaviors are taken into account to examine the impacts on the utility of following lane-changing vehicles.A random parameters logit model is developed to capture the driver psychological heterogeneity in the consecutive lane-changing situation.Furthermore, a lane-changing utility prediction model is established based on three supervised learning algorithms to detect the improper lane-changing decision. Results indicate that (1) the consecutive lane-changing behaviors have a significant negative effect on the following lane-changing vehicles after lane-change; (2) the stimulative effect exists in a consecutive lane-change situation and its influence is heterogeneous due to different psychological activities of drivers; and (3) the utility prediction model can be used to detect an improper lane-changing decision.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 53 references that are in the map. First posted on arXiv 18 May 2022.

Builds on

2 of 53 references.

References · 2

  1. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  2. 2022How to choose features to improve prediction performance in lane-changing intention: A meta-analysiscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.