6 papers · 1 filter
Reinforcement Learning Enhancement Using Vector Semantic Representation and Symbolic Reasoning for Human-Centered Autonomous Emergency Braking
Vinal Asodia, Iman Sharifi, Saber Fallah
The problem with existing camera-based Deep Reinforcement Learning approaches is twofold: they rarely integrate high-level scene context into the feature representation, and they r…
Offline Reinforcement Learning using Human-Aligned Reward Labeling for Autonomous Emergency Braking in Occluded Pedestrian Crossing
Vinal Asodia, Barkin Dagda, Yinglong He +2
Effective leveraging of real-world driving datasets is crucial for enhancing the training of autonomous driving systems. While Offline Reinforcement Learning enables training auton…
Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
Iman Sharifi, Mustafa Yildirim, Saber Fallah
The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving. Deep reinforcement learning…
Neural Lyapunov Function Approximation with Self-Supervised Reinforcement Learning
Luc McCutcheon, Bahman Gharesifard, Saber Fallah
Control Lyapunov functions are traditionally used to design a controller which ensures convergence to a desired state, yet deriving these functions for nonlinear systems remains a…
Behavioral Cloning Models Reality Check for Autonomous Driving
Mustafa Yildirim, Barkin Dagda, Vinal Asodia +1
How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control? While numerous vision-based autonomous vehicle…
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model
Mustafa Yildirim, Barkin Dagda, Saber Fallah
Autonomous driving is a complex task which requires advanced decision making and control algorithms. Understanding the rationale behind the autonomous vehicles' decision is crucial…