End-to-end driving models that learn directly from sensor inputs to control commands, including transformer-based, imitation learning and reinforcement learning approaches.
Frozen perception foundation models encode rich geometric, semantic, and dynamic knowledge.
Engineering 5.5 · Research 8.5 · Business 5.0
We propose an end-to-end collaborative driving system that directly optimizes planning task performance.
Engineering 5.5 · Research 8.0 · Business 5.0
We propose Li-ViP3D++, a query-based multimodal PnP framework that introduces Query-Gated Deformable Fusion (QGDF) to integrate multi-view RGB and LiDAR in query space.
Engineering 6.0 · Research 7.0 · Business 5.0
To enhance unmanned ground vehicle (UGV) intelligence in smart cities, disaster rescue, and infrastructure inspection, this paper investigates the collaborative optimization of multimodal fusion end-to-end architectures.
Engineering 6.5 · Research 7.0 · Business 5.5
Autonomous driving has become a transformative technology poised to reshape modern transportation systems.
Engineering 5.5 · Research 7.0 · Business 5.0
To address these limitations, this paper proposes an end-to-end traffic scene recognition network based on the fusion of monocular camera images and corresponding road map top-down view data.
Engineering 5.5 · Research 7.0 · Business 5.0
DeeperRadar is a radar-centric, sensor-stack-conditioned framework that co-designs radar sensing and multi-modal 3D detection for autonomous mobility by learning a sparse acquisition pattern end-to-end with the fusion model.
Engineering 6.0 · Research 7.0 · Business 6.0
We present DRIFT, a fixed-depth planner that combines one-step drifting in a compact trajectory latent space with scene-aware proposal aggregation.
Engineering 5.5 · Research 7.0 · Business 5.0
Autonomous vehicles (AVs) represent a foundational cornerstone of future smart city transportation systems, offering the potential to eliminate human driving errors, reduce traffic fatalities by at least 40%, and optimize energy consumption.
Engineering 6.5 · Research 7.0 · Business 7.0
World model (WM)-based reinforcement learning enables sample-efficient end-to-end autonomous driving learning by imagining long-horizon trajectories in latent space.
Engineering 5.5 · Research 7.0 · Business 5.0
To address these limitations, we introduce MAMMOTH (MAsking Multi-Modal inputs for Off-road Traversability Heuristic-informed navigation), a unified end-to-end navigation policy for robust off-road visual-goal-conditioned navigation and undirected exploration.
Engineering 6.0 · Research 7.0 · Business 5.5
To address this gap, we present HINT, a two-phase framework for hierarchical ADS fault localization based on hypothesis validation and intent analysis.
Engineering 6.0 · Research 7.0 · Business 6.5
Artificial intelligence (AI) systems are rapidly evolving from passive predictors into autonomous decision-makers that perceive, reason, and act across complex environments.
Engineering 6.0 · Research 7.5 · Business 6.5
In this paper, we present BucketKD, a bucket-based knowledge distillation framework that yields compact and safety-aware end-to-end planners.
Engineering 6.0 · Research 8.0 · Business 6.0
The paper presents the architecture of an intelligent agent for end-to-end autonomous vehicle control based on the M3Drive neural network and multimodal data fusion in the CARLA simulation environment.
Engineering 5.5 · Research 7.0 · Business 5.0
We introduce BeyondSight, a permanence-aware end-to-end driving framework that decouples actor existence from observability by maintaining persistent actor hypotheses over time.
Engineering 6.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Latency-Aware Digital Twin-Assisted Cooperative Perception for Autonomous Vehicles.
Engineering 5.5 · Research 7.0 · Business 5.0
To address this limitation, we propose WCog-VLA, a novel dual-level World-Cognitive VLA framework that successfully bridges semantic world forecasting with generative world evolution to achieve proactive autonomous driving.
Engineering 5.5 · Research 8.0 · Business 5.0
End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in autonomous driving.
Engineering 5.5 · Research 7.0 · Business 5.0
To unify these aspects within a single framework, we propose CARLA-GS, a modular corner-case synthesis pipeline that decouples visual representation, semantic reasoning, and physics-based execution while maintaining tight cross-module coupling.
Engineering 6.0 · Research 7.0 · Business 6.0