ArticleNature communications2025
Predicting driving comfort in autonomous vehicles using road information and multi-head attention models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Emerging contaminants follow class-specific mechanistic regimes in sediment-water partitioning.Environmental science and ecotechnology · 2026Article
- Embodied cognition-driven interpretable trajectory prediction of autonomous systems.Nature communications · 2026Article
- Unravelling depth-dependent pedoclimatic controls on measurable soil organic carbon fractions across climatic gradients in Australian agricultural soils.Scientific reports · 2026Article
- Curvy Surface Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Physics-Aware Spatiotemporal Consistency for Transferable Defense of Autonomous Driving Perception.Sensors (Basel, Switzerland) · 2026Article
- Spectrally encoded parallel LiDAR driven by super-bunching light.Nature communications · 2025Article
- S2R-Bench: A Sim-to-Real Evaluation Benchmark for Autonomous Driving.Scientific data · 2025Article
- Improving Intelligent Vehicle Control with a Prediction Model of Passenger Comfort Based on Postural Instability Parameters.Sensors (Basel, Switzerland) · 2025Article
- Predicting driving comfort in autonomous vehicles using road information and multi-head attention models.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Driving comfort is a crucial consideration in the automotive industry. In the realm of autonomous driving, comfort has always been a factor that requires continuous improvement. A common approach to improving driving comfort is through the optimization of local path planning. Nevertheless, it is imperative to recognize that macroscopic factors, including traffic flow and road conditions, wield a substantial influence on comfort. For instance, complex traffic scenarios increase the possibility of emergency braking, thereby affecting comfort. Consequently, investigating the intricate interplay between comfort and global path planning becomes essential. This paper introduces a methodology and framework for predicting driving comfort by leveraging road information. The study established a road information-driving comfort dataset and devised prediction models using multi-head attention mechanism. The ensuing discussion elucidates the practical application of the model in path planning through examples and tests. Following the path optimized by the model, the vehicles exhibited a reduction in jerk. This research predicted driving comfort based on road information and integrated it with global path planning, which holds significant implications for autonomous driving navigation systems and provides a valuable reference for related research.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.