Proactive Motion Planning Codes for Emergency Collision Avoidance in Highway Scenarios

DOI:10.4121/c4c3015e-702a-43dc-9eed-33b9d207604e.v2
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/c4c3015e-702a-43dc-9eed-33b9d207604e
Datacite citation style:
Gharavi, Leila (2025): Proactive Motion Planning Codes for Emergency Collision Avoidance in Highway Scenarios. Version 2. 4TU.ResearchData. software. https://doi.org/10.4121/c4c3015e-702a-43dc-9eed-33b9d207604e.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Software

choose version:
version 2 - 2025-01-30 (latest)
version 1 - 2023-09-21

This repository includes local motion planners for emergency collision avoidance in automated driving systems. These planners incorporate stochastic prediction models for other road users (e.g. vehicles or static obstacles) and a dynamic prediction model for the ego vehicle. Further, the planners are formulated as model predictive control optimization problems and are designed to find a reference trajectory for the ego vehicle to avoid collision with the road users/obstacles and road boundaries while taking into account the uncertainty in predicting the behavior of other road users.

History

  • 2023-09-21 first online
  • 2025-01-30 published, posted

Publisher

4TU.ResearchData

Format

MLX file including MATLAB codes and functions

Funding

  • Control of Evasive Manoeuvres for Automated Driving: Solving the Edge Cases (EVOLVE) (grant code 18484) NWO Open Technology Programme

Organizations

TU Delft, Faculty Mechanical, Maritime and Materials Engineering (3ME), Delft Center for Systems and Control

DATA

Files (1)

  • 26,752 bytesMD5:9c4537b429381f002244f6707926ff9fSMPC.mlx