LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator
In this paper, we focus on building an efficient deep-learning model for self-driving cars.
Engineering 5.5 · Research 8.0 · Business 5.0
Autonomous Driving Papers with Code, Datasets and Engineering Notes
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.
Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving
This Paper proposes a novel Transformer-based end-to-end autonomous driving model named Detrive.
Engineering 5.5 · Research 8.0 · Business 5.0
Deep Neural Network-based Residual Self- interference Cancellation Method for In-band Full- duplex Underwater Acoustic Communications
We propose a deep neural network-based residual self-interference cancellation (DNN-RSIC) method to handle residual self-interference after analog or spatial domain SIC.
Engineering 5.0 · Research 7.0 · Business 5.0
LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning
To address this issue, we introduce LeTFuser, a lightweight transformer-based algorithm for fusing multiple RGB-D camera representations.
Engineering 7.5 · Research 7.0 · Business 5.5
Deep Learning Based Segmentation Approach for Automatic Lane Detection in Autonomous Vehicle
Lane detection is an essential feature of autonomous vehicle systems, and it can be done automatically.
Engineering 5.0 · Research 7.0 · Business 5.0
Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users
This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology.
Engineering 5.0 · Research 7.0 · Business 5.0
Terrain Depth Estimation for Improved Inertial Data Prediction in Autonomous Navigation Systems
The prediction of terrain elevation values is a key task when it comes to off-road dynamics and inertial data estimation.
Engineering 6.5 · Research 7.0 · Business 5.0
MonoVAN: Visual Attention for Self-Supervised Monocular Depth Estimation
In this paper, we propose a novel fully convolutional network for monocular depth estimation, called MonoVAN, which incorporates the visual attention mechanism and applies super-resolution techniques in decoder to better capture fine-grained details in depth maps.
Engineering 6.5 · Research 8.0 · Business 5.0
A Compact 3D Lane Auto Labeling Method for Autonomous Driving
In this paper, we propose a compact road-reconstruction based 3D lane auto labeling method for autonomous driving, termed R2-3DLane.
Engineering 5.5 · Research 7.0 · Business 6.0
On the Scalability of Parking Trajectory Optimization of Autonomous Ground Vehicles
Specifically, the two-stage optimization process involves first the use of the A star algorithm for initial path generation, and in the second stage, Sequential Quadratic Programming (SQP) is used to optimize the results pathways.
Engineering 5.5 · Research 7.0 · Business 6.5
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
This paper presents an initial step toward leveraging LLMs as effective decision-makers for intricate AD scenarios in terms of safety, efficiency, generalizability, and interoperability.
Engineering 5.0 · Research 7.5 · Business 5.0
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving
We introduce a unique objectlevel multimodal LLM architecture that merges vectorized numeric modalities with a pre-trained LLM to improve context understanding in driving situations.
Engineering 5.0 · Research 7.5 · Business 5.0
Predicting Future Spatiotemporal Occupancy Grids with Semantics for Autonomous Driving
We propose an environment prediction framework that incorporates environment semantics for future occupancy prediction.
Engineering 6.0 · Research 7.0 · Business 6.5
Graph-Theoretic Bézier Curve Optimization over Safe Corridors for Safe and Smooth Motion Planning
In this paper, we present a unifying graph-theoretic perspective for defining and understanding Bézier curve optimization objectives using a consensus distance of Bézier control points derived based on their interaction graph Laplacian.
Engineering 5.0 · Research 7.0 · Business 5.0
DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model
Multimodallarge language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-textual data, including images and videos.
Engineering 5.5 · Research 8.5 · Business 5.0
Finding and Tracking Automobiles on Roads for Self-Driving Car Systems
Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal.
Engineering 5.5 · Research 7.0 · Business 6.5
Self-Driving Car Based CNN Deep Learning Model
An autonomous driving research paper: Self-Driving Car Based CNN Deep Learning Model.
Engineering 5.0 · Research 7.0 · Business 5.0
GAIA-1: A Generative World Model for Autonomous Driving
To address this challenge, we introduce GAIA-1 ('Generative AI for Autonomy'), a generative world model that leverages video, text, and action inputs to generate realistic driving scenarios while offering fine-grained control over ego-vehicle behavior and scene features.
Engineering 5.5 · Research 7.0 · Business 5.5
An End-to-End Autonomous Driving Pre-trained Transformer Model for Multi-Behavior-Optimal Trajectory Generation
To this end, this work tackles the problem by introducing a pre-training method for E2E planning, which can generate multiple initial near-optimal trajectories for further fine-tuning with specific datasets.
Engineering 5.5 · Research 7.0 · Business 5.0
V2I-BEVF: Multi-modal Fusion Based on BEV Representation for Vehicle-Infrastructure Perception
The V2I-BEVF algorithm proposed in this paper experimentally verified on the open-source roadside DAIR-V2X-I dataset from Tsinghua University and Baidu.
Engineering 6.5 · Research 7.0 · Business 5.0