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.
In this study, we propose an obstacle avoidance program for the small drone Tello using depth estimation from a monocular camera image without using a depth sensor.
Engineering 5.5 · Research 7.0 · Business 5.5
In this article, we propose a new self-supervised learning framework based on superpixel and normal constraints to address these problems.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we propose a geometry-guided auto-resizable kernel transformer (GARKT) method, which is designed especially for vehicles with tire blow-out.
Engineering 6.0 · Research 7.0 · Business 5.0
The results show that our model achieves an F1-score of 0.9909, outperforming Unet and LinkNet architectures.
Engineering 5.0 · Research 7.0 · Business 5.5
Multi-Sensor Fusion (MSF) is pivotal in advancing autonomous driving technologies.
Engineering 5.0 · Research 7.0 · Business 5.0
Recently, Self-Supervised Learning (SSL) has achieved great success in various famous applications e.g., BERT and ChatGPT.
Engineering 5.0 · Research 7.0 · Business 5.0
An end-to-end methodology for training convolutional neural networks (CNN) is proposed in this paper for multi class classification of mobile robots using pre-trained weights.
Engineering 5.5 · Research 7.0 · Business 5.0
Accurate 3D image recognition, critical for autonomous driving safety, is shifting from the LIDAR-based point cloud to camera-based depth estimation technologies driven by cost considerations and the point cloud’s limitations in detecting distant small objects.
Engineering 5.5 · Research 8.0 · Business 6.0
Many existing end-to-end autonomous driving methods involve reinforcement learning or multi-stage discrete task pipelines.
Engineering 6.0 · Research 8.0 · Business 5.0
This paper proposes a methodology for object detection and distance estimation within a ROS2 environment, utilizing the fusion of 2D LiDAR and camera sensors.
Engineering 5.0 · Research 7.0 · Business 5.0
We propose a new depth estimation framework that utilizes unlabeled 360-degree data effectively.
Engineering 6.5 · Research 8.0 · Business 5.0
In this paper, we investigate the performance of four reference 3D object detection techniques, when the input PCs are compressed with varying levels of degradation.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: PARA-Drive: Parallelized Architecture for Real-Time Autonomous Driving.
Engineering 5.0 · Research 7.0 · Business 5.0
Finally, we present AdaptiveDriver, a model-predictive control (MPC) based planner that unrolls different world models conditioned on Behavior-Net's predictions.
Engineering 6.0 · Research 8.0 · Business 5.5
This raises a crucial research question: how can we develop better scene feature representations to fully leverage sensor data in end-to-end driving?
Engineering 8.0 · Research 8.0 · Business 5.5
The core functionality of advanced driver assistance systems and self-driving cars depends on the ability to recognize drivable road areas.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper proposes an EFFicient Occupancy learning framework, EFFOcc, that targets minimal network complexity and label requirements while achieving state-of-the-art accuracy.
Engineering 7.5 · Research 8.0 · Business 7.0
To fulfill the paramount need of comprehensive, realistic, and fair testing environments for Full Self-Driving (FSD), we present Bench2Drive, the first benchmark for evaluating E2E-AD systems' multiple abilities in a closed-loop manner.
Engineering 6.0 · Research 8.5 · Business 5.0
This paper proposes a novel AV planning framework that combines deep learning-based behavior prediction and optimization-based uncertainty-aware motion planning to resolve these challenges.
Engineering 5.0 · Research 8.0 · Business 5.0
Inspired by the great power of the neural language model, we propose an end-to-end framework, which transfers the planning task as a language sequence generation task conditioned on pixel inputs.
Engineering 5.5 · Research 8.0 · Business 5.0