An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors

05/22/2020
by   Peng Hang, et al.
0

This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is applied to solve the decision-making, which is formulated as a non-cooperative game problem. Besides, potential field is adopted in the motion planning model, which uses different potential functions to describe surrounding vehicles with different behaviors and road constrains. Then, Model Predictive Control (MPC) is utilized to predict the state and trajectory of the autonomous vehicle. Finally, the decision-making and motion planning is then integrated into a constrained multi-objective optimization problem. Three testing scenarios considering different social behaviors of surrounding vehicles are carried out to validate the performance of the proposed approach. Testing results show that the integrated approach is able to address different social interactions with other traffic participants, and make proper and safe decisions and planning for autonomous vehicles, demonstrating its feasibility and effectiveness.

READ FULL TEXT

page 3

page 6

page 7

page 8

page 9

05/22/2020

Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

Considering that human-driven vehicles and autonomous vehicles (AVs) wil...
01/26/2022

A Cooperation-Aware Lane Change Method for Autonomous Vehicles

Lane change for autonomous vehicles (AVs) is an important but challengin...
01/31/2023

Interaction and Decision Making-aware Motion Planning using Branch Model Predictive Control

Motion planning for autonomous vehicles sharing the road with human driv...
02/14/2023

Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework

With the development of autonomous driving, it is becoming increasingly ...
09/27/2019

Interactive Decision Making for Autonomous Vehicles in Dense Traffic

Dense urban traffic environments can produce situations where accurate p...
11/26/2020

An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

Tactical decision making and strategic motion planning for autonomous hi...
07/08/2021

Reinforcement Learning based Negotiation-aware Motion Planning of Autonomous Vehicles

For autonomous vehicles integrating onto roadways with human traffic par...

Please sign up or login with your details

Forgot password? Click here to reset