From the 1 of 6 linked papers with an AI index.
6 papers
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
Zhouchonghao Wu, Akshay Rangesh, Weixin Li +5
TerraZero is a procedural driving simulator that enables large-scale, zero‑demonstration self‑play reinforcement learning for autonomous driving, achieving high simulation speed an…
ECoSim: Data Efficient Fine-Tuning for Controllable Traffic Simulation
Yu-Hsiang Chen, Wei-Jer Chang, Yi-Ting Chen +1
Controllable traffic simulation is critical for testing autonomous driving systems, yet existing approaches often require retraining large generative models with extensive annotate…
Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos
Matthew Strong, Wei-Jer Chang, Quentin Herau +4
Ego-centric driving videos available online provide an abundant source of visual data for autonomous driving, yet their lack of annotations makes it difficult to learn representati…
HetroD: A High-Fidelity Drone Dataset and Benchmark for Autonomous Driving in Heterogeneous Traffic
Yu-Hsiang Chen, Wei-Jer Chang, Christian Kotulla +7
We present HetroD, a dataset and benchmark for developing autonomous driving systems in heterogeneous environments. HetroD targets the critical challenge of navi- gating real-world…
SPACeR: Self-Play Anchoring with Centralized Reference Models
Wei-Jer Chang, Akshay Rangesh, Kevin Joseph +4
Developing autonomous vehicles (AVs) requires not only safety and efficiency, but also realistic, human-like behaviors that are socially aware and predictable. Achieving this requi…
LANGTRAJ: Diffusion Model and Dataset for Language-Conditioned Trajectory Simulation
Wei-Jer Chang, Wei Zhan, Masayoshi Tomizuka +2
Evaluating autonomous vehicles with controllability enables scalable testing in counterfactual or structured settings, enhancing both efficiency and safety. We introduce LangTraj,…