Browse curated autonomous driving papers on end-to-end driving, BEV perception, 3D object detection, motion prediction, path planning, ADAS, Tesla FSD, Waymo, and self-driving foundation models.
Due to the advancement in smart technologies like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and sophisticated sensors, self-driving cars are emerging as a significant innovation in the automotive sector.
Engineering 5.0 · Research 7.0 · Business 5.0
The primary input for motion planning, which permits safe autonomous driving on public roads, is an accurate trajectory prediction of nearby road users.
Engineering 5.0 · Research 7.0 · Business 5.0
In this work, we design a fully sparse framework, SparseAlign, with three key features: an enhanced sparse 3D backbone, a query-based temporal context learning module, and a robust detection head specially tailored for sparse features.
Engineering 5.0 · Research 8.0 · Business 5.0
Our contributions are threefold: (1) We introduce a dual-branch network to optimize depth maps and propose a multi-scale channel spatial attention module (MS_CBAM).
Engineering 5.0 · Research 7.0 · Business 5.0
We propose an efficient adaptation of an uncertainty estimation technique for 3D occupancy prediction.
Engineering 7.0 · Research 7.0 · Business 5.5
To this end, we propose a coarse-to-fine Transformer for multimodal trajectory prediction, i.e., Pioformer, which explicitly extracts the post-interaction features to enhance the prediction accuracy.
Engineering 5.0 · Research 7.0 · Business 5.0
Therefore, we propose a model that can handle three different tasks: (1) closed-loop driving, (2) vision-language understanding, and (3) language-action alignment.
Engineering 5.0 · Research 8.5 · Business 5.0
To address these challenges, we introduce PCLA (Pretrained CARLA Leaderboard Agents), an open-source Python testing framework that includes nine high-performing pre-trained autonomous agents from the Leaderboard challenges.
Engineering 6.5 · Research 7.0 · Business 5.0
To overcome both challenges, we propose a plug-and-play method called JiSAM, shorthand for Jittering augmentation, domain-aware backbone and memory-based Sectorized AlignMent.
Engineering 6.0 · Research 7.0 · Business 5.5
In this paper, we propose AlphaDrive, a RL and reasoning framework for VLMs in autonomous driving.
Engineering 5.5 · Research 7.0 · Business 5.0
In this paper, we present a purely vision-based transformer model for end-to-end automatic parking, trained using expert trajectories.
Engineering 5.5 · Research 8.0 · Business 5.0
End-to-end autonomous driving (E2E-AD) has emerged as a trend in the field of autonomous driving, promising a data-driven, scalable approach to system design.
Engineering 6.0 · Research 8.0 · Business 5.0
We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories.
Engineering 7.0 · Research 8.0 · Business 5.0
Unmanned aerial vehicles (UAVs) are widely employed across diverse fields due to their flexibility and scalability.
Engineering 5.0 · Research 7.0 · Business 5.0
But it is not just about finding the best route: Our approach also ensures safer and more natural driving by optimizing trajectories and avoiding sudden movements.
Engineering 5.5 · Research 7.0 · Business 5.5
In this paper, we present a novel 3D occupancy prediction approach, H30, which features highly efficient architecture designs that incur a significantly lower computational cost as compared to the current state-of-the-art methods.
Engineering 5.5 · Research 8.0 · Business 5.0
This paper presents a set of exemplary conflict scenarios in CARLA that arise in shared autonomy settings, where both AVs and human drivers must navigate complex traffic environments.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous valet parking is an L4-level self-driving technology that solves the “last-mile freedom” of automobile users.
Engineering 5.0 · Research 7.0 · Business 6.0
To address this, we propose a multi-level hierarchical model AdaptiveOcc.
Engineering 7.0 · Research 8.0 · Business 6.0
To address these challenges, commencing with the representation of state-action mapping in the end-to-end autonomous driving paradigm, we introduce a novel pipeline, VDT-Auto.
Engineering 6.5 · Research 8.0 · Business 5.5