Predicting Cut-in Events Using LSTM Based Techniques Mercedes Benz R&D, Pvt., Limited

Format:

Book

Conference/Event

Author/Creator:

Srivastava, Rohan, author.

Contributor:

Bhattacharya, Abhinov

Nayak, Apoorva S.

Satwik, Rahul

Suvvari, Sai Dileep

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Road accidents involving cut-in and sudden brake events on highways present major challenges to driver safety, often outpacing the response time of traditional Advanced Driver Assistance Systems (ADAS). The objective of this study is to predict potential collisions caused by cut-ins before ADAS intervention becomes necessary, allowing for earlier driver alerts and enhanced vehicle response. The proposed method employs machine learning and deep learning approaches, specifically Long Short-Term Memory (LSTM) networks, to forecast collision risks 0.5 to 3 seconds in advance. Synthetic data generation techniques are used to create rare but critical cut-in and braking scenarios, complementing real-world data from test vehicles and accident records. Key predictive features monitored include relative velocity, lateral velocity, and lane overlap, which provide dynamic indicators of imminent risk. Results show that the system achieves an average early warning time of 1.35 seconds in 40.206% of evaluated hazardous scenarios, significantly improving the chance for evasive maneuvers and collision avoidance. Compared to conventional reactive systems, our approach proactively identifies threats by integrating real-time sensing with predictive modeling. The conclusion drawn from this research is that combining synthetic event generation with LSTM-based predictive analytics can substantially enhance ADAS capabilities, reduce accident rates, and pave the way for smarter, more anticipatory vehicle safety systems. These findings offer an important advancement toward more intelligent road safety technologies that emphasize prevention rather than reaction

Notes:

Vendor supplied data

Publisher Number:

2026-26-0636

Access Restriction:

Restricted for use by site license