# A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems

arXiv: https://arxiv.org/abs/2305.01096 · HTML: https://mlsanity.com/paper/2305.01096/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 2 of 2)

- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019)

## Built on by (most cited first, 2 of 2)

- [SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset](https://mlsanity.com/paper/2401.01425/) (2024)
- [From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps](https://mlsanity.com/paper/2601.14848/) (2026)

## Abstract (arXiv)

Accurate lane change prediction can reduce potential accidents and contribute to higher road safety. Adaptive cruise control (ACC), lane departure avoidance (LDA), and lane keeping assistance (LKA) are some conventional modules in advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle communication (V2V), vehicles can share traffic information with surrounding vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies on the vehicle's sensors to obtain the position and velocity of the leading vehicle, CACC also has access to the acceleration of multiple vehicles through V2V communication. This paper compares the type of information (position, velocity, acceleration) and the number of surrounding vehicles for driver lane change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD dataset to predict lane change intention. Results indicate a significant improvement in accuracy with an increase in the number of surrounding vehicles and the information received from them. Specifically, the proposed model can predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and CACC scenarios, respectively.