arXiv:2606. 16313v1 Announce Type: cross Abstract: Long-tail scenarios remain a major bottleneck for autonomous driving evaluation, even as datasets grow by orders of magnitude.
arXiv:2607. 07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety.
arXiv:2609.37871v1 Announce Type: cross Abstract: Average performance on routine driving benchmarks does not establish planner reliability under rare, safety-critical hazards. We proposed ExceptionDr...
arXiv:2605.27690v2 Announce Type: replace-cross Abstract: LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate st...
Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic safety. These scenarios are severely under-represented in naturalistic driving data, and existing trajectory and language-augmented datasets seldom provide high-risk event labels, semantic annotations, and verifiable safety signals.
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.
The paper audits runtime failure monitors that use a model’s internal representations to predict failures in autonomous driving tasks. Across two tasks—online vectorized map generation with LaneSegNet and end‑to‑end planning with VAD—the authors find that frame‑level errors can be predicted with high AUROC scores using supervised latent probes. However, adding latent features to baseline monitors that use only observable inputs and outputs does not yield statistically significant improvements, suggesting that internal representations may not provide additional predictive value beyond what is already observable.
arXiv:2601.01762v4 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes....
arXiv:2604. 02022v4 Announce Type: replace Abstract: Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses.
The paper proposes a new training dataset that generates more informative positive and negative samples for trajectory scoring in autonomous driving. By perturbing logged human trajectories laterally toward the drivable boundary and longitudinally toward a leading vehicle, the dataset provides richer supervision than the planner’s default proposal pool. Using a transformer-based scorer trained on this dataset, the authors achieve improved EPDMS scores on two frozen planners, DiffusionDrive and MeanFuser, when evaluated on the NAVSIM navtrain dataset.
HERMES is a holistic end‑to‑end multimodal driving framework that incorporates long‑tail semantic knowledge into trajectory planning for autonomous vehicles. It uses a foundation‑model‑assisted annotation pipeline to build Long‑Tail Scene Context and Long‑Tail Planning Context, capturing hazard‑centric scene information, maneuver intent, and risk‑aware guidance. A Tri‑Modal Driving Module then fuses multi‑view visual observations, historical ego‑motion, and long‑tail semantic instructions to generate intent‑ and risk‑aware trajectories, achieving consistent performance gains on a large‑scale real‑world long‑tail driving benchmark.
arXiv:2609.10377v1 Announce Type: cross Abstract: Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities...