8 papers
STRIDE: Evaluating Spatiotemporal Reasoning in Driving Edge Cases
Lieqi Liu, Rui gao, Jia-Chen Gu +6
In autonomous driving, perceiving surrounding objects is not enough: failing to understand how they move and interact over time can lead to dangerous decisions. Evaluating vision-l…
Directional Pullback Regularization for Long-Horizon Rollout of Latent World Models in Autonomous Driving
Shatadal Mishra, Ahmadreza Moradipari, Nejib Ammar
Learned world models for autonomous driving operate in unconstrained latent spaces, yet the physical systems they represent are governed by strict non-holonomic constraints: ground…
Network-Efficient World Model Token Streaming
Shatadal Mishra, Ahmadreza Moradipari, Nejib Ammar
Generative driving world models rely on compact latent state representations that must be efficiently transmitted and synchronized across distributed compute and connected vehicles…
Agentic AI for Trip Planning Optimization Application
Tiejin Chen, Ahmadreza Moradipari, Kyungtae Han +2
Trip planning for intelligent vehicles increasingly requires selecting optimal routes rather than merely producing feasible itineraries, as interacting factors such as travel time,…
On-Policy Distillation of Language Models for Autonomous Vehicle Motion Planning
Amirhossein Afsharrad, Amirhesam Abedsoltan, Ahmadreza Moradipari +1
Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction as a language generation prob…
Formation and Investigation of Cooperative Platooning at the Early Stage of Connected and Automated Vehicles Deployment
Zeyu Mu, Sergei S. Avedisov, Ahmadreza Moradipari +1
Cooperative platooning, enabled by cooperative adaptive cruise control (CACC), is a cornerstone technology for connected automated vehicles (CAVs), offering significant improvement…
SIMSplat: Language-Aligned 4D Gaussian Splatting for Driving Scenario Generation
Sung-Yeon Park, Adam Lee, Juanwu Lu +6
Driving scene manipulation using real-world sensor data has emerged as a promising alternative to traditional driving simulators. Despite advances in language control and neural sc…
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning
Dechen Gao, Hang Wang, Hanchu Zhou +5
Imitation learning (IL) and reinforcement learning (RL) each offer distinct advantages for robotics policy learning: IL provides stable learning from demonstrations, and RL promote…