arXiv Computer Vision
Robust Offset-Free Lane-Keeping Control Framework for Vision-Only Automated Vehicles
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arXiv Computer Vision
Oct 26
SAGE: A hybrid vision-language and geometry framework for work-zone pathway prediction under temporary traffic control
arXiv:2610.31835v1 Announce Type: new Abstract: Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a...
arXiv Machine Learning
Sep 22
Correcting Learning-based Perception for Safety
The paper presents a two-step method to correct machine‑learning based perception for safety in autonomous systems. First, it uses offline computation to characterize uncertainties from the ML module via preimages of perception contracts. Then, at runtime, a risk heuristic selects specific states from these uncertain estimates to guide control decisions, reducing safety violations in adaptive cruise control scenarios while adding minimal delay.
arXiv Computer Vision
2d ago
MotoVerse: A Benchmark for Predictive Perception of Motorized Two-Wheelers in Dense Unstructured Traffic
arXiv:2611.06007v1 Announce Type: new Abstract: Motorized Two-Wheelers (MTWs) dominate personal transport across much of the Global South, yet remain largely overlooked in predictive perception resea...
arXiv Computer Vision
Oct 27
Computer Vision Navigation for an Autonomous Vehicle Model Using Reinforcement Learning
arXiv:2610.33755v1 Announce Type: new Abstract: This work aims to assess the capabilities of the reinforcement learning algorithm, Proximal Policy Optimization (PPO), in developing a policy capable o...
arXiv AI
2d ago
Traffic-VL: Reinforced Vision-Language Models for Generalizable End-to-End Traffic Signal Control
Traffic-VL introduces a vision‑language model that directly maps synchronized multi‑view images and natural‑language phase descriptions to traffic signal decisions, bypassing intermediate structured states. It is trained using SynTra‑V, a photorealistic simulation that generates surveillance imagery from physics‑based traffic models, and employs offline reinforcement learning to adapt its vision encoder before freezing it for online control. Experiments demonstrate that Traffic‑VL matches the performance of structured‑state controllers while outperforming image‑based and general‑purpose vision‑language models, and it generalizes robustly to unseen topologies, degraded sensing, real footage, and real‑world deployment across multiple intersections.
arXiv AI
Sep 10
Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies
The paper studies how Bird's‑Eye‑View (BEV) maps predicted by Cross‑View Transformers (CVT) can be used directly as inputs to a Behavior‑Cloning (BC) driving policy in the CARLA simulator. It introduces a six‑channel BEV representation and a Kernel Density Estimation (KDE) weighting scheme to focus learning on underrepresented maneuvers. Closed‑loop tests show that the KDE‑weighted model is the only predicted‑BEV agent to finish an episode without infractions, highlighting that global segmentation scores are poor proxies for driving performance and that prediction quality at critical geometries, especially the route channel, is key to reliable navigation.