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...
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: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: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...
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.
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.
BridgeGuard is a safety‑constrained diffusion planning method for autonomous driving that progressively strengthens a constraint term during denoising, guiding intermediate trajectories toward a scene‑dependent safety domain. It operates in a low‑dimensional curve space and uses a learned DistanceFieldNet to predict a time‑dependent distance field from bird’s‑eye‑view features, providing safety injection while keeping the perception backbone and planner frozen. Experiments on Bench2Drive show that BridgeGuard improves driving scores and success rates across two diffusion‑based models, demonstrating cross‑model generalization.
The paper presents a lane‑change prediction model using knowledge‑graph embeddings and Bayesian inference to anticipate surrounding vehicles’ intentions 3–4 seconds before they occur. Three driving strategies—reactive, prediction‑based longitudinal, and prediction‑based lateral—are compared in CARLA simulations of risky merging scenarios. Prediction‑based approaches avoid collisions, reduce peak deceleration by up to 91 %, and are evaluated for ride comfort using ISO 2631‑1 vibration estimates.
arXiv:2606. 16558v1 Announce Type: new Abstract: Roundabouts challenge automated driving in mixed traffic, as heterogeneous and non-deterministic human behavior, unknown driving intentions, and high interaction complexity create uncertainty about whether the conflict zone will be blocked or available at the moment of entry.
The paper evaluates five vision‑language models on autonomous driving tasks under various visual input conditions, finding that visual corruption affects accuracy and confidence differently across models and datasets. It then tests Visual Evidence Augmentation (VEA) as an inference‑time technique to enhance reliability, observing mixed improvements depending on the model and setting.
arXiv:2606. 26661v1 Announce Type: cross Abstract: Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios.
The paper introduces a geo‑referenced smartphone dashcam dataset of 63,839 frames from South Texas and a baseline framework for assessing road lane quality. Three new metrics—Contrast Stability Index (CSI), Line Continuity Ratio (LCR), and Regression Lane Confidence Score (RLCS)—are proposed to evaluate lane‑marking visibility, continuity, and map consistency without dense human labels. Baseline models achieve strong performance, and pilot validation shows the metrics correlate better with human judgments than generic image‑quality measures.