Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching

03/05/2020
by   Lu Zhang, et al.
0

Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e.g., tracking noise and prediction errors, etc.). Although the partially observable Markov decision process (POMDP) provides a systematic way to incorporate these uncertainties, it quickly becomes computationally intractable when scaled to the real-world large-size problem. In this paper, we present an efficient uncertainty-aware decision-making (EUDM) framework, which generates long-term lateral and longitudinal behaviors in complex driving environments in real-time. The computation complexity is controlled to an appropriate level by two novel techniques, namely, the domain-specific closed-loop policy tree (DCP-Tree) structure and conditional focused branching (CFB) mechanism. The key idea is utilizing domain-specific expert knowledge to guide the branching in both action and intention space. The proposed framework is validated using both onboard sensing data captured by a real vehicle and an interactive multi-agent simulation platform. We also release the code of our framework to accommodate benchmarking.

READ FULL TEXT
research
01/15/2021

Interaction-Aware Behavior Planning for Autonomous Vehicles Validated with Real Traffic Data

Autonomous vehicles (AVs) need to interact with other traffic participan...
research
09/27/2019

Interactive Decision Making for Autonomous Vehicles in Dense Traffic

Dense urban traffic environments can produce situations where accurate p...
research
03/29/2023

Intention-Aware Decision-Making for Mixed Intersection Scenarios

This paper presents a white-box intention-aware decision-making for the ...
research
08/04/2022

AG2U – Autonomous Grading Under Uncertainties

Surface grading, the process of leveling an uneven area containing pre-d...
research
04/05/2021

FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation

A limitation for collaborative robots (cobots) is their lack of ability ...
research
03/02/2020

Decision-making for automated vehicles using a hierarchical behavior-based arbitration scheme

Behavior planning and decision-making are some of the biggest challenges...
research
09/25/2020

Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World

We propose a multi-agent based computational framework for modeling deci...

Please sign up or login with your details

Forgot password? Click here to reset