Search Results for author:
Found 14 papers, 3 papers with code
Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning
We further use unsupervised contrastive representation learning as an auxiliary task to improve the sample efficiency.
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply complicated negotiation skills with them, such as yielding, merging and taking turns, to achieve both safe and efficient driving in various settings.
SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection
In particular, SNE-RoadSeg, our previously proposed method based on a surface normal estimator (SNE) and a data-fusion DCNN (RoadSeg), has achieved impressive performance in freespace detection.
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs.
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving
With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance.
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
To address this problem, we propose an interpretable end-to-end vision-based motion planning approach for autonomous driving, referred to as IVMP.
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching
Supervised learning with deep convolutional neural networks (DCNNs) has seen huge adoption in stereo matching.
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation
Therefore, this paper mainly explores an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available.
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable cases.
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
Freespace detection is an essential component of visual perception for self-driving cars.
Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion
All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors.
VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments
Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
High-speed Autonomous Drifting with Deep Reinforcement Learning
Drifting is a complicated task for autonomous vehicle control.
