arXiv AI
Jun 8
Planning-aligned Token Compression for Long-Context Autonomous Driving
arXiv:2606. 07464v1 Announce Type: cross Abstract: Monolithic vision-action models represent an emerging paradigm in autonomous driving.
Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey
arXiv:2304. 10891v3 Announce Type: replace-cross Abstract: Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies, multi-agent interactions, and multimodal context across perception, prediction, and planning.
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arXiv AI
Jun 6
Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving
arXiv:2601. 21288v2 Announce Type: replace Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain.
Hugging Face Trending Papers
Jul 1
Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation
Self-Supervised Monocular Depth Estimation (MDE) has garnered attention in recent years due to its independence from ground truth. However, most existing models are limited to a single scale and exhibit considerable performance degradation in complex driving environments.
arXiv AI
Jun 10
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
arXiv:2602. 23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable.
arXiv AI
Aug 10
WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA
arXiv:2608. 01035v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving; however, their efficient deployment is severely constrained by high computational latency and exposure bias arising from sequential autoregressive decoding.
arXiv AI
Jun 6
CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving
arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.
arXiv AI
Jul 28
Real-Time Semantic Segmentation with Optimized RetinaNet Architectures for Embedded Automotive Systems
arXiv:2607. 22714v1 Announce Type: cross Abstract: Real-time perception is a foundational requirement for advanced driver assistance systems (ADAS) and autonomous vehicles, yet embedded automotive platforms impose severe constraints on compute, memory, and power.
arXiv AI
Jul 14
Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles
arXiv:2607. 10942v1 Announce Type: cross Abstract: Physical AI systems, such as autonomous vehicles and intelligent machines, require transformer-based perception models that satisfy stringent edge latency and energy constraints.
arXiv AI
Aug 11
OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.
arXiv AI
2d ago
UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures
arXiv:2608. 16696v1 Announce Type: cross Abstract: Physical AI systems such as autonomous vehicles and robots rely on timely exchange of high-dimensional sensory signals under tight bandwidth, latency, and energy budgets.
arXiv AI
Jun 19
Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving
arXiv:2606. 20274v1 Announce Type: new Abstract: Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and planners that produce kinematically valid trajectories.
Hugging Face Trending Papers
Jun 24
UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving
Diffusion models have shown strong potential for multi-modal planning in end-to-end autonomous driving. However, most existing methods confine diffusion to the planning module, conditioning on fixed outputs from separate discriminative perception networks.