30 citations · 127 across the 33 of their papers we have counts for
16 papers · 1 filter
A Neural Network Based Teleoperation for Remote Controlled Vehicles
Ning Ding, Azim Eskandarian
Direct teleoperation of vehicles faces critical technical bottlenecks: communication latency and the operator's inability to physically perceive unmodeled environmental disturbance…
Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning
Shathushan Sivashangaran, Vihaan Dutta, Apoorva Khairnar +2
Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestrian fatalities and $500 billio…
Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing
Shathushan Sivashangaran, Apoorva Khairnar, Sepideh Gohari +2
Autonomous racing without prebuilt maps is a grand challenge for embedded robotics that requires kinodynamic planning from instantaneous sensor data at the acceleration and tire fr…
Prediction uncertainty-aware planning using deep ensembles and trajectory optimisation
Anshul Nayak, Azim Eskandarian
Human motion is stochastic and ensuring safe robot navigation in a pedestrian-rich environment requires proactive decision-making. Past research relied on incorporating determinist…
Mobile Robot Exploration Without Maps via Out-of-Distribution Deep Reinforcement Learning
Shathushan Sivashangaran, Apoorva Khairnar, Azim Eskandarian
Autonomous Mobile Robot (AMR) navigation in dynamic environments that may be GPS denied, without a-priori maps, is an unsolved problem with potential to improve humanity's capabili…
Cooperative Probabilistic Trajectory Forecasting under Occlusion
Anshul Nayak, Azim Eskandarian
Perception and planning under occlusion is essential for safety-critical tasks. Occlusion-aware planning often requires communicating the information of the occluded object to the…
Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty
Anshul Nayak, Azim Eskandarian, Zachary Doerzaph +1
One of the fundamental challenges in the prediction of dynamic agents is robustness. Usually, most predictions are deterministic estimates of future states which are over-confident…
Probabilistic RRT Connect with intermediate goal selection for online planning of autonomous vehicles
Darshit Patel, Azim Eskandarian
Rapidly Exploring Random Trees (RRT) is one of the most widely used algorithms for motion planning in the field of robotics. To reduce the exploration time, RRT-Connect was introdu…
AutoVRL: A High Fidelity Autonomous Ground Vehicle Simulator for Sim-to-Real Deep Reinforcement Learning
Shathushan Sivashangaran, Apoorva Khairnar, Azim Eskandarian
Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazar…
Deep Reinforcement Learning for Autonomous Ground Vehicle Exploration Without A-Priori Maps
Shathushan Sivashangaran, Azim Eskandarian
Autonomous Ground Vehicles (AGVs) are essential tools for a wide range of applications stemming from their ability to operate in hazardous environments with minimal human operator…
XTENTH-CAR: A Proportionally Scaled Experimental Vehicle Platform for Connected Autonomy and All-Terrain Research
Shathushan Sivashangaran, Azim Eskandarian
Connected Autonomous Vehicles (CAVs) are key components of the Intelligent Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs) are indispensable tools fo…
Emergency Collision Avoidance and Mitigation Using Model Predictive Control and Artificial Potential Function
Xu Shang, Azim Eskandarian
Although extensive research in emergency collision avoidance has been carried out for straight or curved roads in a highway scenario, a general method that could be implemented for…
X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research Powered by CARMA
Goodarz Mehr, Prasenjit Ghorai, Ce Zhang +4
Autonomous vehicles promise a future with a safer, cleaner, more efficient, and more reliable transportation system. However, the current approach to autonomy has focused on buildi…
Sentinel: An Onboard Lane Change Advisory System for Intelligent Vehicles to Reduce Traffic Delay during Freeway Incidents
Goodarz Mehr, Azim Eskandarian
This paper introduces Sentinel, an onboard system for intelligent vehicles that guides their lane changing behavior during a freeway incident with the goal of reducing traffic cong…
Estimating the Probability that a Vehicle Reaches a Near-Term Goal State Using Multiple Lane Changes
Goodarz Mehr, Azim Eskandarian
This paper proposes a model to estimate the probability of a vehicle reaching a near-term goal state using one or multiple lane changes based on parameters corresponding to traffic…
Integrating Inter-vehicular Communication, Vehicle Localization, and a Digital Map for Cooperative Adaptive Cruise Control with Target Detection Loss
Yuan Lin, Azim Eskandarian
Adaptive Cruise Control (ACC) is an Advanced Driver Assistance System (ADAS) that enables vehicle following with desired inter-vehicular distances. Cooperative Adaptive Cruise Cont…