1.8k citations · 1.9k across the 10 of their papers we have counts for
10 papers
ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles
Cosmin Ginerica, Mihai Zaha, Florin Gogianu +3
A key component in autonomous driving is the ability of the self-driving car to understand, track and predict the dynamics of the surrounding environment. Although there is signifi…
OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots
Bogdan Trasnea, Cosmin Ginerica, Mihai Zaha +3
Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan t…
Embedded Vision for Self-Driving on Forest Roads
Sorin Grigorescu, Mihai Zaha, Bogdan Trasnea +1
Forest roads in Romania are unique natural wildlife sites used for recreation by countless tourists. In order to protect and maintain these roads, we propose RovisLab AMTU (Autonom…
LVD-NMPC: A Learning-based Vision Dynamics Approach to Nonlinear Model Predictive Control for Autonomous Vehicles
Sorin Grigorescu, Cosmin Ginerica, Mihai Zaha +2
In this paper, we introduce a learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles, coined LVD-NMPC. LVD-NMPC uses an a-priori proc…
Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles
Sorin Grigorescu, Tiberiu Cocias, Bogdan Trasnea +3
Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such…
A Survey of Deep Learning Techniques for Autonomous Driving
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias +1
The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The…
AIBA: An AI Model for Behavior Arbitration in Autonomous Driving
Bogdan Trasnea, Claudiu Pozna, Sorin Grigorescu
Driving in dynamically changing traffic is a highly challenging task for autonomous vehicles, especially in crowded urban roadways. The Artificial Intelligence (AI) system of a dri…
NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles
Sorin Grigorescu, Bogdan Trasnea, Liviu Marina +2
Autonomous vehicles are controlled today either based on sequences of decoupled perception-planning-action operations, either based on End2End or Deep Reinforcement Learning (DRL)…
Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding
Liviu Marina, Bogdan Trasnea, Cocias Tiberiu +3
Grid maps obtained from fused sensory information are nowadays among the most popular approaches for motion planning for autonomous driving cars. In this paper, we introduce Deep G…
GridSim: A Vehicle Kinematics Engine for Deep Neuroevolutionary Control in Autonomous Driving
Bogdan Trasnea, Andrei Vasilcoi, Claudiu Pozna +1
Current state of the art solutions in the control of an autonomous vehicle mainly use supervised end-to-end learning, or decoupled perception, planning and action pipelines. Anothe…