30 citations · 127 across the 33 of their papers we have counts for
34 papers
Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation
Sepideh Gohari, Goodarz Mehr, Azim Eskandarian
Standard deployment-ready object detectors for autonomous vehicles degrade in adverse weather and lighting conditions without being trained on extensive domain-specific data. While…
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…
SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perception
Goodarz Mehr, Sepideh Gohari, Montasir Abbas +1
Cooperative perception through vehicle-to-everything (V2X) communication can overcome the inherent physical limitations of individual autonomous vehicles, such as occlusions and li…
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…
SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset
Goodarz Mehr, Azim Eskandarian
Bird's-eye view (BEV) perception has garnered significant attention in autonomous driving in recent years, in part because BEV representation facilitates multi-modal sensor fusion.…
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…
Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving
Ce Zhang, Azim Eskandarian
LiDAR is one of the most crucial sensors for autonomous vehicle perception. However, current LiDAR-based point cloud perception algorithms lack comprehensive and rigorous LiDAR qua…
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…
The Impact of Different Backbone Architecture on Autonomous Vehicle Dataset
Ning Ding, Azim Eskandarian
Object detection is a crucial component of autonomous driving, and many detection applications have been developed to address this task. These applications often rely on backbone a…
SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving
Ning Ding, Ce Zhang, Azim Eskandarian
Object detection (OD) is crucial to autonomous driving. On the other hand, unknown objects, which have not been seen in training sample set, are one of the reasons that hinder auto…
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…
EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals
Ning Ding, Ce Zhang, Azim Eskandarian
A lack of driver's vigilance is the main cause of most vehicle crashes. Electroencephalography(EEG) has been reliable and efficient tool for drivers' drowsiness estimation. Even th…
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…
Uncertainty estimation of pedestrian future trajectory using Bayesian approximation
Anshul Nayak, Azim Eskandarian, Zachary Doerzaph
Past research on pedestrian trajectory forecasting mainly focused on deterministic predictions which provide only point estimates of future states. These future estimates can help…
A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception
Ce Zhang, Azim Eskandarian
Reliable object detection using cameras plays a crucial role in enabling autonomous vehicles to perceive their surroundings. However, existing camera-based object detection approac…
Attention-based Neural Network for Driving Environment Complexity Perception
Ce Zhang, Azim Eskandarian, Xuelai Du
Environment perception is crucial for autonomous vehicle (AV) safety. Most existing AV perception algorithms have not studied the surrounding environment complexity and failed to i…
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification
Ce Zhang, Young-Keun Kim, Azim Eskandarian
Classification of EEG-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural ne…