Self-Driving Cars
169 papers with code • 0 benchmarks • 15 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
These leaderboards are used to track progress in Self-Driving Cars
Libraries
Use these libraries to find Self-Driving Cars models and implementations
DDAD
Lost and Found
Shifts
Talk2Car
BDD-X
Argoverse 2 Motion Forecasting
SOD
HPD
OC
ELAS
Latest papers with no code
A Hybrid Generative and Discriminative PointNet on Unordered Point Sets
This paper proposes GDPNet, the first hybrid Generative and Discriminative PointNet that extends JEM for point cloud classification and generation.
Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
This review looks back and analyzes the current state of achievements in the field of 3D lane detection research.
PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot
The study of non-line-of-sight (NLOS) imaging is growing due to its many potential applications, including rescue operations and pedestrian detection by self-driving cars.
ENet-21: An Optimized light CNN Structure for Lane Detection
Lane detection for autonomous vehicles is an important concept, yet it is a challenging issue of driver assistance systems in modern vehicles.
Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving
This paper presents a novel multi-modal Multi-Object Tracking (MOT) algorithm for self-driving cars that combines camera and LiDAR data.
Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection
In this work, we propose a novel framework for real-time LiDAR odometry and mapping based on LOAM architecture for fast moving platforms.
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles.
Distilling Adversarial Robustness Using Heterogeneous Teachers
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e. g., self-driving cars or medical imaging.
Multi-class Temporal Logic Neural Networks
Time-series data can represent the behaviors of autonomous systems, such as drones and self-driving cars.
Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and Evaluation on a Custom Track Environment
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.
