Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning

09/29/2020
by   Klemens Esterle, et al.
0

Autonomous vehicles need to abide by the same rules that humans follow. Some of these traffic rules may depend on multiple agents or time. Especially in situations with traffic participants that interact densely, the interactions with other agents need to be accounted for during planning. To study how multi-agent and time-dependent traffic rules shall be modeled, a framework is needed that restricts the behavior to rule-conformant actions during planning, and that can eventually evaluate the satisfaction of these rules. This work presents a method to model the conformance to traffic rules for interactive behavior planning and to test the ramifications of the traffic rule formulations on metrics such as collision, progress, or rule violations. The interactive behavior planning problem is formulated as a dynamic game and solved using Monte Carlo Tree Search, for which we contribute a new method to integrate history-dependent traffic rules into a decision tree. To study the effect of the rules, we treat it as a multi-objective problem and apply a relaxed lexicographical ordering to the vectorized rewards. We demonstrate our approach in a merging scenario. We evaluate the effect of modeling and combining traffic rules to the eventual compliance in simulation. We show that with our approach, interactive behavior planning while satisfying even complex traffic rules can be achieved. Moving forward, this gives us a generic framework to formalize traffic rules for autonomous vehicles.

READ FULL TEXT

page 1

page 6

03/05/2020

BARK: Open Behavior Benchmarking in Multi-Agent Environments

Predicting and planning interactive behaviors in complex traffic situati...
07/01/2020

Formalizing Traffic Rules for Machine Interpretability

Autonomous vehicles need to be designed to abide by the same rules that ...
02/05/2021

Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving

Balancing safety and efficiency when planning in dense traffic is challe...
05/02/2019

From Specifications to Behavior: Maneuver Verification in a Semantic State Space

To realize a market entry of autonomous vehicles in the foreseeable futu...
10/30/2019

Automatic Testing and Falsification with Dynamically Constrained Reinforcement Learning

We consider the problem of using reinforcement learning to train adversa...
07/11/2020

Feedback Enhanced Motion Planning for Autonomous Vehicles

In this work, we address the motion planning problem for autonomous vehi...
11/04/2020

IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data

Autonomous vehicles (AVs) need to share the road with multiple, heteroge...