1 citations · 1 across the 13 of their papers we have counts for
13 papers
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi +5
Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, multi-agent interactions common i…
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
Hamidreza Mirkhani, Behzad Khamidehi, Ehsan Ahmadi +6
In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses…
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories
Niloufar Saeidi Mobarakeh, Behzad Khamidehi, Chunlin Li +6
The primary goal of motion planning is to generate safe and efficient trajectories for vehicles. Traditionally, motion planning models are trained using imitation learning to mimic…
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning
Fazel Arasteh, Mohammed Elmahgiubi, Behzad Khamidehi +4
The planning problem constitutes a fundamental aspect of the autonomous driving framework. Recent strides in representation learning have empowered vehicles to comprehend their sur…
Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge
Weize Zhang, Mohammed Elmahgiubi, Kasra Rezaee +8
In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place.…
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories
Hamidreza Mirkhani, Behzad Khamidehi, Kasra Rezaee
Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. However, imitating trajectories that inadequately represent the original expert da…
Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots
Behzad Khamidehi, Elvino S. Sousa
This paper studies the traffic monitoring problem in a road network using a team of aerial robots. The problem is challenging due to two main reasons. First, the traffic events are…
Dynamic Resource Management for Providing QoS in Drone Delivery Systems
Behzad Khamidehi, Majid Raeis, Elvino S. Sousa
Drones have been considered as an alternative means of package delivery to reduce the delivery cost and time. Due to the battery limitations, the drones are best suited for last-mi…
Federated Learning for Cellular-connected UAVs: Radio Mapping and Path Planning
Behzad Khamidehi, Elvino S. Sousa
To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many appl…
No Cross-Validation Required: An Analytical Framework for Regularized Mixed-Integer Problems (Extended Version)
Behrad Soleimani, Behzad Khamidehi, Maryam Sabbaghian
This paper develops a method to obtain the optimal value for the regularization coefficient in a general mixed-integer problem (MIP). This approach eliminates the cross-validation…
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint
Behzad Khamidehi, Elvino S. Sousa
This paper studies the trajectory optimization problem for an aerial vehicle with the mission of flying between a pair of given initial and final locations. The objective is to min…
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations
Behzad Khamidehi, Elvino S. Sousa
In this paper, the trajectory optimization problem for a multi-aerial base station (ABS) communication network is investigated. The objective is to find the trajectory of the ABSs…
Power Efficient Trajectory Optimization for the Cellular-Connected Aerial Vehicles
Behzad Khamidehi, Elvino S. Sousa
Aerial vehicles have recently attracted significant attention in a variety of commercial and civilian applications due to their high mobility, flexible deployment and cost-effectiv…