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.
To address these limitations, we introduce TPS-Drive, a novel framework centered on Task-Guided Representation Purification that empowers VLMs to Think in Purified Space.
Engineering 5.5 · Research 8.0 · Business 5.0
An autonomous driving research paper: A review of learning-based motion planning: toward a data-driven optimal control approach.
Engineering 5.0 · Research 7.0 · Business 5.0
The growth of electric vehicles leads to an increase in end-of-life battery systems, requiring scalable and safe disassembly processes for sustainable recycling.
Engineering 5.5 · Research 7.0 · Business 5.5
To address these challenges, we present G-DRAGON, a retrieval-augmented framework for outdoor, open-world navigation.
Engineering 5.5 · Research 8.5 · Business 5.5
Inspired by error-correction notebooks used in learning practice, we design a novel multi-level replay buffer mechanism.
Engineering 6.0 · Research 8.0 · Business 5.5
In this work, we propose an end-to-end spiking encoder-decoder network for object detection in bird's eye view representations of LiDAR point clouds, trained using surrogate gradient backpropagation.
Engineering 6.0 · Research 8.0 · Business 6.5
Accurate 3D bird's-eye view (BEV) object detection is essential for autonomous driving, and depends strongly on effective multimodal representations from complementary sensors such as cameras and LiDAR.
Engineering 5.5 · Research 7.0 · Business 5.0
Autonomous driving stacks must pick one trajectory from a multi-modal candidate set; choosing by model confidence ignores safety, traffic-law, and comfort constraints.
Engineering 5.5 · Research 7.0 · Business 6.0
In this paper, we present SparseWorld, a lightweight world model that focuses on predicting only the critical layout of the scene, enabling efficient future forecasting for end-to-end driving systems.
Engineering 7.5 · Research 8.0 · Business 5.0
This paper proposes HOPNet (Heterogeneous Object Priority Network), a multi-modal real-time object detection framework integrating YOLOv8-based convolutional neural networks with Swin Transformer attention, camera-LiDAR fusion, and domain-adaptive transfer learning.
Engineering 5.0 · Research 8.0 · Business 5.0
To address this gap, we present MR-LiDAR, a controlled multi-resolution LiDAR benchmark for roadside perception diagnostics.
Engineering 5.5 · Research 7.0 · Business 6.0
We propose NudgeVAD, a frozen-planner residual framework that uses language as a calibrated nudge to a VAD trajectory.
Engineering 5.5 · Research 8.0 · Business 5.0
An autonomous driving research paper: Spatio-temporal adaptive refinement for bird’s eye view cooperative perception.
Engineering 5.0 · Research 7.0 · Business 5.0
This article analyzes the legal and regulatory foundations of the U.S.
Engineering 5.0 · Research 7.0 · Business 5.0
We propose a SlowFast-based 3-D bidirectional feature pyramid network (FPN) with multidimensional attention (SF3D-MDA) mechanisms.
Engineering 7.5 · Research 7.0 · Business 6.5
This paper provides an in-depth exploration of a design and implementation of multi-sensor perception system for autonomous vehicle (AV).
Engineering 5.5 · Research 7.0 · Business 6.0
An autonomous driving research paper: E2ETrADS: end-to-end transformer based autonomous driving system for adverse weather conditions.
Engineering 5.5 · Research 7.0 · Business 5.0
Motion planning for autonomous vehicles requires generating collision-free and dynamically feasible trajectories in complex environments under real-time constraints.
Engineering 5.0 · Research 7.0 · Business 5.0
We present MORPH-U, a CARLA-based closed-loop stack that fuses LiDAR/radar/camera with V2X (CAM/DENM) into a Local Dynamic Map (LDM) and triggers Hybrid-A* replanning when validated hazards or map changes affect the planned route.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous path planning and collision avoidance (CA) in complex maritime environments are critical challenges for maritime autonomous surface ships (MASSs).
Engineering 5.0 · Research 8.0 · Business 5.0