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cs.RO2023
End-to-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic
Jatan Shrestha, Simon Idoko, Basant Sharma +1
Trajectory sampling in the Frenet(road-aligned) frame, is one of the most popular methods for motion planning of autonomous vehicles. It operates by sampling a set of behavioural i…
cs.RO2022
Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic
Arun Kumar Singh, Jatan Shrestha, Nicola Albarella
Autonomous driving has a natural bi-level structure. The goal of the upper behavioural layer is to provide appropriate lane change, speeding up, and braking decisions to optimize a…
cs.RO2020
GPU Accelerated Convex Approximations for Fast Multi-Agent Trajectory Optimization
Fatemeh Rastgar, Houman Masnavi, Jatan Shrestha +3
In this paper, we present a computationally efficient trajectory optimizer that can exploit GPUs to jointly compute trajectories of tens of agents in under a second. At the heart o…