Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

Safe Motion Planning for Autonomous Driving

Self-driving cars have the potential to revolutionize transportation by making it cheaper, safer, and more efficient. In this thesis we describe a novel motion planning system, which translates high-level navigation goals into low-level actions for controlling a vehicle. Specifically, the motion planning system is responsible for choosing at each time step an appropriate velocity and steering a...

Real-Time Motion Planning 
 and
 Autonomous Driving

Toward Safe and Human-like Motion Generation for Autonomous Driving

1. Project Overview In order to drive safely and efficiently, autonomous vehicles need to understand the behavior of others, predict their future motions and plan desirable motions which are safe and human-like to execute. In this project, we tackled motion prediction and planning together as a motion generation problem to achieve accurate comprehension of others, as well as safe and human-like...

A Lightweight Simulator for Autonomous Driving Motion Planning Development

A good simulation environment will facilitate motion planning algorithm development for urban autonomous driving. The first requirement of such a simulator is to be able to replicate a complex urban environment, including road network, curb, general objects, etc. The second requirement is to simulate a realistic host vehicle, which includes perception, control and vehicle dynamics, to recreate ...

A New Algorithm for Load Flow Analysis in Autonomous Networks

In this paper, a novel algorithm for the load flow analysis problem in an islanded microgrid is proposed. The problem is modeled without any slack bus by considering the steady state frequency as one of the load flow variables. To model different control modes of DGs, such as droop, PV and PQ, in an islanded microgrid, a new formula for load flow equations is proposed. A hybrid optimization alg...