Authors:
(1) Mehdi Naderi;
(2) Markos Papageorgiou;
(3) Dimitrios Troullinos;
(4) Iasson Karafyllis;
(5) Ioannis Papamichail. Table of Links Abstract and Introduction Vehicle Modeling The Nonlinear Feedback Control OD Corridors and Desired Orientations Boundary and Safety Controllers Simulation Results Conclusion Appendix A: Collision Detection Appendix B: Transformed ISO-Distance curves Appendix C: Local Density Appendix D: Safety Controller Details Appendix E: Controller Parameters References APPENDIX D: SAFETY CONTROLLER DETAILS A. Finding cross-point when EV and obstacle have circular and skewed motions, respectively In this case, the cross-point can be found by crossing the following movement equations: So, the cross-point’s longitudinal position is one of the roots of the following second-order polynomial that results from (46) after simplification: B. Finding cross-point when EV and obstacle have skewed and circular motions, respectively Following a similar procedure to the previous one, the crosspoint can be found by calculating roots of the following equation: The found cross-points should be transformed to the coordinates aligned with the EV’s desired orientation. If they are behind the EV or obstacle or the roots are not real, they are ignored. Otherwise, the closest cross-point is considered. C. Finding the closest current obstacle This paper is available on arxiv under CC 4.0 license. Authors: (1) Mehdi Naderi; (2) Markos Papageorgiou; (3) Dimitrios Troullinos; (4) Iasson Karafyllis; (5) Ioannis Papamichail. Authors: Authors: (1) Mehdi Naderi; (2) Markos Papageorgiou; (3) Dimitrios Troullinos; (4) Iasson Karafyllis; (5) Ioannis Papamichail. Table of Links Abstract and Introduction Abstract and Introduction Vehicle Modeling Vehicle Modeling The Nonlinear Feedback Control The Nonlinear Feedback Control OD Corridors and Desired Orientations OD Corridors and Desired Orientations Boundary and Safety Controllers Boundary and Safety Controllers Simulation Results Simulation Results Conclusion Conclusion Appendix A: Collision Detection Appendix A: Collision Detection Appendix B: Transformed ISO-Distance curves Appendix B: Transformed ISO-Distance curves Appendix C: Local Density Appendix C: Local Density Appendix D: Safety Controller Details Appendix D: Safety Controller Details Appendix E: Controller Parameters Appendix E: Controller Parameters References References APPENDIX D: SAFETY CONTROLLER DETAILS A. Finding cross-point when EV and obstacle have circular and skewed motions, respectively A. Finding cross-point when EV and obstacle have circular and skewed motions, respectively In this case, the cross-point can be found by crossing the following movement equations: So, the cross-point’s longitudinal position is one of the roots of the following second-order polynomial that results from (46) after simplification: B. Finding cross-point when EV and obstacle have skewed and circular motions, respectively B. Finding cross-point when EV and obstacle have skewed and circular motions, respectively Following a similar procedure to the previous one, the crosspoint can be found by calculating roots of the following equation: The found cross-points should be transformed to the coordinates aligned with the EV’s desired orientation. If they are behind the EV or obstacle or the roots are not real, they are ignored. Otherwise, the closest cross-point is considered. C. Finding the closest current obstacle C. Finding the closest current obstacle This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Handling Complex Vehicle Movements: Techniques for Finding Cross-Points and Obstacles

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Improving Privacy Risk Detection with Sequence Labelling and Web Search

X Is Auto-Loading Your Links—Affiliates Just Found a Way to Turn It Into CPM Cash

An AI Crew for Automated Diagramming and Documentation

How I Built an AI-Powered Research Automation System with n8n, Groq, and 5 Academic APIs

The AI Reality Gap: What 11 Professionals Revealed About AI at Work That MIT Studies Won't Tell You

Rapid-eks – Production EKS in 13 minutes with Terraform + Python

Improving Privacy Risk Detection with Sequence Labelling and Web Search

X Is Auto-Loading Your Links—Affiliates Just Found a Way to Turn It Into CPM Cash

An AI Crew for Automated Diagramming and Documentation

How I Built an AI-Powered Research Automation System with n8n, Groq, and 5 Academic APIs

The AI Reality Gap: What 11 Professionals Revealed About AI at Work That MIT Studies Won't Tell You

Rapid-eks – Production EKS in 13 minutes with Terraform + Python

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps