334 citations · 445 across the 25 of their papers we have counts for
28 papers
Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning
Luc McCutcheon, Evangelos Chatzaroulas, Saber Fallah
Neural networks are hindered by accumulating dormant neurons and loss of expressivity throughout training, particularly in non-stationary data settings, such as continual supervise…
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
Iman Sharifi, Peng Wei, Saber Fallah
Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probab…
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…
GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching
Barkin Dagda, Muhammad Awais, Saber Fallah
Cross-view geo-localisation identifies coarse geographical position of an automated vehicle by matching a ground-level image to a geo-tagged satellite image from a database. Despit…
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…
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…
Human-Like Autonomous Driving on Dense Traffic
Mustafa Yildirim, Saber Fallah
This paper proposes a imitation learning model for autonomous driving on highway traffic by mimicking human drivers' driving behaviours. The study utilizes the HighD traffic datase…
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
Iman Sharifi, Mustafa Yildirim, Saber Fallah
Current imitation learning approaches, predominantly based on deep neural networks (DNNs), offer efficient mechanisms for learning driving policies from real-world datasets. Howeve…
Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach
Zahra Chaghazardi, Saber Fallah, Alireza Tamaddoni-Nezhad
Traffic sign detection is a critical task in the operation of Autonomous Vehicles (AV), as it ensures the safety of all road users. Current DNN-based sign classification systems re…
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…
Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays
Luc McCutcheon, Saber Fallah
Local-remote systems allow robots to execute complex tasks in hazardous environments such as space and nuclear power stations. However, establishing accurate positional mapping bet…
Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction
Salar Arbabi, Davide Tavernini, Saber Fallah +1
Autonomous agents that drive on roads shared with human drivers must reason about the nuanced interactions among traffic participants. This poses a highly challenging decision maki…
Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning
Mehran Raisi, Amirhossein Noohian, Luc Mccutcheon +1
This paper proposes a novel scoring function for the planning module of MPC-based reinforcement learning methods to address the inherent bias of using the reward function to score…
Learning an Interpretable Model for Driver Behavior Prediction with Inductive Biases
Salar Arbabi, Davide Tavernini, Saber Fallah +1
To plan safe maneuvers and act with foresight, autonomous vehicles must be capable of accurately predicting the uncertain future. In the context of autonomous driving, deep neural…
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks
Sajjad Mozaffari, Eduardo Arnold, Mehrdad Dianati +1
Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records. Thus, reliably predicting such manoeuvre in advance is crit…
ARC: Adversarially Robust Control Policies for Autonomous Vehicles
Sampo Kuutti, Saber Fallah, Richard Bowden
Deep neural networks have demonstrated their capability to learn control policies for a variety of tasks. However, these neural network-based policies have been shown to be suscept…
Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data
Sampo Kuutti, Saber Fallah, Richard Bowden
Imitation learning has been widely used to learn control policies for autonomous driving based on pre-recorded data. However, imitation learning based policies have been shown to b…
Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments
Marco Visca, Sampo Kuutti, Roger Powell +2
Terrain traversability analysis plays a major role in ensuring safe robotic navigation in unstructured environments. However, real-time constraints frequently limit the accuracy of…
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments
Marco Visca, Arthur Bouton, Roger Powell +2
Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-boa…
Self-adaptive Torque Vectoring Controller Using Reinforcement Learning
Shayan Taherian, Sampo Kuutti, Marco Visca +1
Continuous direct yaw moment control systems such as torque-vectoring controller are an essential part for vehicle stabilization. This controller has been extensively researched wi…
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
Sampo Kuutti, Richard Bowden, Saber Fallah
The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies prese…
Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways
Salar Arbabi, Shilp Dixit, Ziyao Zheng +3
Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-co…