LAUSR

Accurate modelling of acceleration and deceleration decisions is vital for replicating car‐following behaviour, as these govern longitudinal control, traffic stability, and safety. This study introduces ArrowNet81, a 200‐layer novel convolutional neural network (CNN) architecture designed to model the asymmetric nature of these decisions using multivariate time‐series data. The architecture leverages modular Ribs and Linkers to enhance depth while minimizing complexity. A perceived‐risk variable derived from nominal time‐to‐collision (NomTTC) addresses the effect of vehicle heterogeneity in car following behaviour. Trajectories extracted from UAV video footage support neighbourhood vehicle identification via the queens move system (QMS), and a novel generalized data arrangement technique ensures compatibility with deep learning (DL) inputs. To prove its generalizability and superiority, the proposed architecture is trained, tested, and validated in three diverse traffic conditions against different statistical, machine learning (ML), and DL techniques. And the developed models using ArrowNet81 architecture outperform all of them. Future research may adapt ArrowNet81 for classification problems such as mode choice, accident severity, or spatial risk mapping, and integrate it into connected and autonomous vehicles (CAVs) to emulate human‐like behaviours. The rib‐based architecture also permits the development of lighter variants, facilitating real‐time deployment in resource‐constrained environments without compromising predictive performance.Accurate modelling of acceleration and deceleration decisions is vital for replicating car‐following behaviour. ArrowNet81, a 200‐layer novel CNN architecture designed to model the asymmetric nature of these decisions using multivariate time‐series data. The rib‐based architecture permits the development of lighter variants, facilitating real‐time deployment in resource‐constrained environments without compromising predictive performance.