DeepAI AI Chat
Log In Sign Up

Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways

07/28/2020
by   Salar Arbabi, et al.
0

Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with safety constraints. It is shown that the decision model can replicate human drivers' discretionary lane-change decisions with up to 92 concept simulation of an overtaking manoeuvre is shown, whereby the actions of the simulated vehicle are logged while the dynamic environment evolves as per ground truth data recordings.

READ FULL TEXT

page 1

page 5

10/19/2020

A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness

Discretionary lane change (DLC) is a basic but complex maneuver in drivi...
11/02/2022

Implementation of Road Safety Perception in Autonomous Vehicles in a Lane Change Scenario

Understanding human driving behavior is crucial to develop autonomous ve...
05/23/2020

Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving

Driving in a human-like manner is important for an autonomous vehicle to...
12/17/2021

Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach

To develop driving automation technologies for human, a human-centered m...
06/06/2022

Lane-Level Route Planning for Autonomous Vehicles

We present an algorithm that, given a representation of a road network i...
01/26/2023

Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing

Although a typical autopilot system far surpasses humans in term of sens...