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 paper, we first introduce the background of vision-based 3D occupancy prediction and discuss the challenges in this task.
Engineering 7.0 · Research 7.0 · Business 6.0
To address this challenge, we propose OmniDrive, a holistic vision-language dataset that aligns agent models with 3D driving tasks through counter-factual reasoning.
Engineering 6.0 · Research 7.0 · Business 5.5
Therefore, this paper presents a cost-effective, scalable incremental learning-based detection method.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we present a novel approach for robustly calibrating the extrinsic parameters of a solid-state(SS) lidar-camera system in a natural environment.
Engineering 5.5 · Research 8.0 · Business 5.5
Perception systems play a crucial role in autonomous driving by reading the sensory data and providing meaningful interpretation of the operating environment for decision-making and planning.
Engineering 5.0 · Research 7.0 · Business 5.0
Recognizing 3D objects from point clouds is a crucial technology for autonomous vehicles.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we analyze the model structure of FastSCNN, a lightweight semantic segmentation network, focus on the performance of FastSCNN in the lane line detection task, and compare it with four other classical models.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these issues, we propose an adaptive motion planner based on human‐like cognition and cost evaluation for off‐road driving.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we introduce OccGen, a simple yet powerful generative perception model for the task of 3D semantic occupancy prediction.
Engineering 5.5 · Research 8.0 · Business 5.0
We present PLUTO, a powerful framework that pushes the limit of imitation learning-based planning for autonomous driving.
Engineering 7.5 · Research 8.0 · Business 5.5
Today, Tesla, Google, Uber, and GM are all trying to create their own self-driving cars that can run on real-world roads and Dominos Pizza, Amazon and Walmart decided to create them own proprietary delivery vehicles for commercial use.
Engineering 6.0 · Research 7.0 · Business 7.5
In this paper, we propose a simple yet effective fully end-to-end framework named Future Instance Prediction Transformer(FipTR), which views the task as BEV instance segmentation and prediction for future frames.
Engineering 7.0 · Research 8.0 · Business 5.0
To address this, we propose SparseOcc, an efficient occupancy network inspired by sparse point cloud processing.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these challenges, we propose a novel Priority-Aware Collaborative Perception (PACP) framework to employ a BEV-match mechanism to determine the priority levels based on the correlation between nearby CAVs and the ego vehicle for perception.
Engineering 5.0 · Research 8.0 · Business 5.0
Specifically, we employ a multi-condition controlled diffusion model.
Engineering 5.5 · Research 8.0 · Business 5.0
To address this problem, we present HawkDrive, a novel perception system with hardware and software solutions.
Engineering 7.0 · Research 8.0 · Business 5.0
Specifically, we first propose a Geometric- and Semantic-aware Fusion (GSFusion) module to explicitly enhance LiDAR features by incorporating neighboring camera features through a K-nearest neighbors (KNN) search.
Engineering 5.5 · Research 8.0 · Business 5.0
In this paper, we want to present our motion planning pipeline with particle swarm optimization (PSO) at its core.
Engineering 5.5 · Research 7.0 · Business 5.5
Ensuring safe and efficient navigation in smart campus transportation systems is increasingly crucial with the integration of autonomous vehicles, making accurate and robust lane detection a paramount factor.
Engineering 5.0 · Research 7.0 · Business 5.0
Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals.
Engineering 5.0 · Research 7.0 · Business 5.0