RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics
This paper proposes a new strategy for a collision avoidance system leveraging time-to-collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating deep learning with TTC calculations, the system predicts potential collisions...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Smart Cities |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6511/8/1/15 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849404625661198336 |
|---|---|
| author | Jamal Raiyn |
| author_facet | Jamal Raiyn |
| author_sort | Jamal Raiyn |
| collection | DOAJ |
| description | This paper proposes a new strategy for a collision avoidance system leveraging time-to-collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC-based approaches. The methodology is validated through extensive simulations, demonstrating a significant improvement in collision avoidance performance compared to traditional TTC-based approaches. By integrating deep learning models with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions. The use of the Gaussian model to contributes to time-to-collision (TTC) analysis by providing a probabilistic framework to quantify collision risk under uncertainty. It calculates the likelihood that TTC will fall below a critical threshold (TTC_crit), indicating a potential collision. By modeling input variations—such as sensor inaccuracies, fluctuating vehicle velocity, and unpredictable driving behavior—as a Gaussian distribution, the system can handle real-world uncertainties more effectively. This enables continuous, real-time risk prediction, allowing for dynamic and adaptive collision avoidance decisions. The Gaussian approach enhances the robustness of TTC-based systems by improving their ability to predict and prevent collisions in uncertain driving conditions. |
| format | Article |
| id | doaj-art-8fb53ddc0d0e4ade93a7618b2431276b |
| institution | Kabale University |
| issn | 2624-6511 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj-art-8fb53ddc0d0e4ade93a7618b2431276b2025-08-20T03:36:57ZengMDPI AGSmart Cities2624-65112025-01-01811510.3390/smartcities8010015RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision MetricsJamal Raiyn0Connected Urban Mobility, Technical University of Applied Sciences Aschaffenburg, 63743 Aschaffenburg, GermanyThis paper proposes a new strategy for a collision avoidance system leveraging time-to-collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC-based approaches. The methodology is validated through extensive simulations, demonstrating a significant improvement in collision avoidance performance compared to traditional TTC-based approaches. By integrating deep learning models with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions. The use of the Gaussian model to contributes to time-to-collision (TTC) analysis by providing a probabilistic framework to quantify collision risk under uncertainty. It calculates the likelihood that TTC will fall below a critical threshold (TTC_crit), indicating a potential collision. By modeling input variations—such as sensor inaccuracies, fluctuating vehicle velocity, and unpredictable driving behavior—as a Gaussian distribution, the system can handle real-world uncertainties more effectively. This enables continuous, real-time risk prediction, allowing for dynamic and adaptive collision avoidance decisions. The Gaussian approach enhances the robustness of TTC-based systems by improving their ability to predict and prevent collisions in uncertain driving conditions.https://www.mdpi.com/2624-6511/8/1/15autonomous vehiclescollision avoidancetime to collisiondeep learningcut-in scenariosensitivity analysis |
| spellingShingle | Jamal Raiyn RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics Smart Cities autonomous vehicles collision avoidance time to collision deep learning cut-in scenario sensitivity analysis |
| title | RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics |
| title_full | RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics |
| title_fullStr | RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics |
| title_full_unstemmed | RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics |
| title_short | RETRACTED: Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics |
| title_sort | retracted improvement in collision avoidance in cut in maneuvers using time to collision metrics |
| topic | autonomous vehicles collision avoidance time to collision deep learning cut-in scenario sensitivity analysis |
| url | https://www.mdpi.com/2624-6511/8/1/15 |
| work_keys_str_mv | AT jamalraiyn retractedimprovementincollisionavoidanceincutinmaneuversusingtimetocollisionmetrics |