Autonomous Driving

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Period ending 2026-09-21

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Autonomous Driving

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Driving

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An Empirical Markov Chain Car-Following (MC-CF) Model
Date pendingSungyong Chung, Yanlin Zhang, Nachuan Li +2Microscopic Traffic Simulations Traffic
Data-Driven Risk Fields for Safer End-to-End Autonomous Driving
Date pendingYuanxin Tian, Zhiyuan Liu, Jinhao Li +9Autonomous Driving Classical Planning
A Multi-Modal Perception Pipeline for Object Detection and Tracking in Autonomous Racing
Date pendingDavide Malvezzi, Michele Pestarino, Vittoria Cavicchioli +9Autonomous Driving Perception Multi-Object Tracking
The Vienna 4G/5G Drive-Test Dataset
Date pendingWilfried Wiedner, Lukas Eller, Mariam Mussbah +4Mobile Networks Naturalistic Driving Data
4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation
Date pendingJimmie Kwok, Holger Caesar, Andras PalffyLight Detection And Ranging Point Clouds Radar
Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Embodiment Adaptation
Date pendingZhidong Wang, Jingsong Liang, Zirui Li +3Autonomous Driving World Models

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An Empirical Markov Chain Car-Following (MC-CF) Model

Sungyong Chung, Yanlin Zhang, Nachuan Li, Dana Monzer+1

Car-following behavior is fundamental to traffic flow theory, yet traditional models often fail to capture the stochasticity of naturalistic driving. This paper proposes an empirical probabilistic sampling approach to car-following modeling that bypasses conventional parametric assumptions. Under this approach, we introduce the Markov Chain Car-Following (MC-CF) model, which represents state transitions as a Markov process and predicts behavior by randomly sampling accelerations from empirical distributions within discretized state bins. Evaluation on the Waymo Open Motion Dataset (WOMD) demonstrates that MC-CF variants significantly outperform all physics-based baselines (IDM, Gipps, FVDM, and SIDM) across both one-step and open-loop trajectory prediction metrics, and remain competitive with modern data-driven baselines including neural network and Gaussian mixture model approaches. Zero-shot generalization on the Naturalistic Phoenix (PHX) dataset further confirms cross-domain transferability. Finally, microscopic ring road simulations validate the framework's scalability: by incrementally integrating unconstrained free-flow trajectories and high-speed freeway data (TGSIM) alongside a conservative inference strategy, the model substantially reduces collisions across most tested scenarios and successfully reproduces naturalistic and stochastic shockwave propagation, though crashes persist under severe shockwave conditions. Overall, the proposed MC-CF model provides a robust and scalable foundation for simulating population-level stochastic traffic behavior that requires no behavioral parameter calibration, making it well-suited for the data-rich future of intelligent transportation.

Microscopic Traffic Simulations Traffic Driving

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