7 papers · 1 filter
Slow Brain, Fast Planner: Latency-Resilient VLM-Augmented Urban Navigation
Zhenghao "Mark'' Peng, Honglin He, Quanyi Li +2
Learning-based planners for sidewalk navigation can generate diverse candidate trajectories in real time, yet their scoring functions often fail to select the best trajectory in ch…
Monocular 3D Occupancy Perception for Robots on Sidewalks via Hybrid 2D-3D Learning
Yukai Ma, Joe Lin, Liu Liu +5
Sidewalks in the real world are crowded, cluttered, and less structured than roads, making 3D occupancy prediction a key ingredient for the safe navigation of mobile robots such as…
From Imitation to Alignment: Human-Preference Flow Policies for Long-Horizon Sidewalk Navigation
Honglin He, Zhizheng Liu, Yukai Ma +1
Autonomous long-horizon sidewalk navigation is essential for micro-mobility applications such as robotic food delivery and assistive electronic wheelchairs. Unlike autonomous drivi…
Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion
Honglin He, Yukai Ma, Brad Squicciarini +2
Sidewalk micromobility is a promising solution for last-mile transportation, but current learning-based control methods struggle in complex urban environments. Imitation learning (…
SceneStreamer: Continuous Scenario Generation as Next Token Group Prediction
Zhenghao Peng, Yuxin Liu, Bolei Zhou
Realistic and interactive traffic simulation is essential for training and evaluating autonomous driving systems. However, most existing data-driven simulation methods rely on stat…
Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation
Yuxin Liu, Zhenghao Peng, Xuanhao Cui +1
Scenario-based testing is essential for validating the performance of autonomous driving (AD) systems. However, such testing is limited by the scarcity of long-tailed, safety-criti…