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 research project presents the implementation of a Deep Q-Learning Network (DQN) for a self-driving car on a 2-dimensional (2D) custom track, with the objective of enhancing the DQN network's performance.
Engineering 5.0 · Research 7.0 · Business 5.0
To this end, we present the first comprehensive systematic literature review of explainable methods for safe and trustworthy AD.
Engineering 5.0 · Research 7.0 · Business 5.0
Scene simulation in autonomous driving has gained significant attention because of its huge potential for generating customized data.
Engineering 7.0 · Research 8.5 · Business 6.0
Autonomous driving has emerged as a transformative technology with the potential to revolutionize transportation.
Engineering 5.5 · Research 8.0 · Business 5.5
To address this issue, we propose hierarchical motion planning and robust control strategy using the front active steering system in complex scenarios with various slippery road adhesion coefficients while considering vehicle uncertain parameters.
Engineering 5.0 · Research 7.0 · Business 5.0
To fill this gap, we propose a transformation-based approach SCTRANS to construct simulation scenario files, utilizing existing traffic scenario datasets (i.e., naturalistic movement of road users recorded on public roads) as data sources.
Engineering 5.0 · Research 8.0 · Business 6.0
To address these challenges, we introduce a Hybrid-Prediction integrated Planning (HPP) framework, which operates through three novel modules collaboratively.
Engineering 6.0 · Research 8.0 · Business 6.0
Our method employs a multi-objective optimization strategy for efficient navigation in static and highly dynamic environments, focusing on optimizing trajectory comfort, safety, and path precision.
Engineering 7.0 · Research 7.0 · Business 5.5
The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD).
Engineering 5.5 · Research 7.5 · Business 5.0
We propose Spiking CenterNet for object detection on event data.
Engineering 5.0 · Research 8.0 · Business 5.0
With the development of society, technological progress, and new needs, autonomous driving has become a trendy topic in smart cities.
Engineering 5.0 · Research 7.0 · Business 5.0
In this work, we propose a method to perform object detection in autonomous driving based on a geometrical and sequential sensor fusion of 3+1D RADAR and semantics extracted from camera data through point cloud painting from the perspective view.
Engineering 5.0 · Research 8.0 · Business 5.5
As a remedy, we propose an effective PTQ method called LiDAR-PTQ, which is particularly curated for 3D lidar detection (both SPConv-based and SPConv-free).
Engineering 6.5 · Research 8.0 · Business 5.0
Our method uses the outputs of a 2D object detector to cut the patches containing objects out of images, and estimate the allocentric orientation of the object in each image patch.
Engineering 5.5 · Research 7.0 · Business 5.5
This paper proposes a first-principles based sensor modeling and environment interaction scheme, and integrates it into CARLA simulator.
Engineering 5.0 · Research 8.0 · Business 5.0
In recent years, LIDARs (Light Detection and Ranging) have gained a lot of insight into various fields such as agriculture, astronomy, robotics, autonomous driving, forestry, etc.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we present VLP, a novel Vision-Language-Planningframework that exploits language models to bridge the gap between linguistic understanding and autonomous driving.
Engineering 6.0 · Research 8.0 · Business 5.0
In this paper, an optimal design method based on BPNN (BP neural network) is adopted to design the intelligent control system of SDA trajectory.
Engineering 5.0 · Research 7.0 · Business 5.0
Specifically, in an experiment, our investigation revealed a notable discrepancy in the LiDAR reflection intensity within a point cloud scene, with stronger intensities observed in proximity and weaker intensities observed at a distance.
Engineering 5.0 · Research 8.0 · Business 5.0
For autonomous driving, various sensors such as cameras, LiDAR, and radar are required to accurately perceive the surrounding environment.
Engineering 5.0 · Research 7.0 · Business 5.0