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.
This paper presents YOLOv11-SEG, an enhanced deep learning model tailored for real-time detection and instance segmentation of road markings and lane lines, crucial for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles.
Engineering 5.5 · Research 8.0 · Business 6.5
Time-triggered systems are widely deployed in safety-critical applications due to their deterministic timing behavior.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these challenges, we propose a novel method, called ESFusion for Effective BEV Feature Selection and Fusion.
Engineering 5.5 · Research 8.0 · Business 5.0
Monocular depth estimation is a rudimentary problem for robotic perception systems and downstream applications.
Engineering 5.0 · Research 7.0 · Business 5.0
Our methodology combines quantitative metrics (lane deviation, mean speed, stopping distance) with qualitative assessments through GUI-based visualizations.
Engineering 5.5 · Research 7.0 · Business 6.5
To address these limitations, we present CShenron, a radar simulation framework integrated into CARLA, which generates realistic radar measurements by fusing LiDAR and camera data.
Engineering 7.5 · Research 7.0 · Business 5.5
An autonomous driving research paper: Gaussian-Based World Model: Gaussian Priors for Voxel-Based Occupancy Prediction and Future Motion Prediction.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose an end-to-end localization neural network which directly estimates vehicle poses from surrounding images, without explicitly matching perception results with HD maps.
Engineering 5.5 · Research 7.0 · Business 5.0
This paper presents a vision-only autonomous flight system for small UAVs operating in controlled indoor environments.
Engineering 6.0 · Research 8.0 · Business 6.5
Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy state or actuator constraints.
Engineering 5.0 · Research 8.0 · Business 5.0
End-to-end autonomous driving requires robust mapping from sequential visual inputs to control actions.
Engineering 5.5 · Research 8.0 · Business 5.0
We propose BEV-ConvFusion, a novel 2D-domain fusion framework that overcomes this limitation.
Engineering 5.5 · Research 8.0 · Business 6.0
An autonomous driving research paper: RPFE-Net: RoI-guided pseudo-LiDAR point cloud feature enhancement network for multi-modal 3D object detection.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, a golf cart with improved autonomous driving capabilities has been used to construct a real-time human tracking application.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we present the design and implementation of an affordable and light-weight depth estimation system executed on a Raspberry Pi 5 platform.
Engineering 5.5 · Research 7.0 · Business 5.5
Panoptic perception models in autonomous driving use deep learning models to interpret their surroundings and make real-time decisions.
Engineering 5.5 · Research 7.0 · Business 5.5
To address these limitations, we present PG-Occ, an innovative Progressive Gaussian Transformer Framework that enables open-vocabulary 3D occupancy prediction.
Engineering 7.0 · Research 8.0 · Business 5.5
In this paper, we propose a realtime, CPU-based ground segmentation method that structures the point cloud into a polar grid and estimates the ground points based on the local features of the grid cells.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: HMambaOcc: Hierarchical Mamba for occupancy flow field prediction in autonomous driving under mixed traffic environments.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Low-latency and energy-efficient FPGA accelerator for sparse neural networks in edge LiDAR-based 3D object detection.
Engineering 5.0 · Research 7.0 · Business 5.0