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Found 8 papers, 1 papers with code
Towards Autonomous and Safe Last-mile Deliveries with AI-augmented Self-driving Delivery Robots
To address the hurdles faced by existing robotic couriers, this paper introduces a customer-centric and safety-conscious LMD system for small urban communities based on AI-assisted autonomous delivery robots.
DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities
With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized.
DFR-FastMOT: Detection Failure Resistant Tracker for Fast Multi-Object Tracking Based on Sensor Fusion
The proposed solution enables superior performance under various distortion levels in detection over current state-of-art methods.
Dual Formulation for Chance Constrained Stochastic Shortest Path with Application to Autonomous Vehicle Behavior Planning
The Constrained Stochastic Shortest Path problem (C-SSP) is a formalism for planning in stochastic environments under certain types of operating constraints.
Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning
A major challenge in machine learning is resilience to out-of-distribution data, that is data that exists outside of the distribution of a model's training data.
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
In this paper, we propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs).
A Fully Polynomial Time Approximation Scheme for Constrained MDPs and Stochastic Shortest Path under Local Transitions
The fixed-horizon constrained Markov Decision Process (C-MDP) is a well-known model for planning in stochastic environments under operating constraints.
Risk-Aware Reasoning for Autonomous Vehicles
Third, a planning subsystem that takes into account the uncertainty, from perception and intention recognition subsystems, and propagates all the way to control policies that explicitly bound the risk of collision.
