# Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems

arXiv: https://arxiv.org/abs/2212.12819 · HTML: https://mlsanity.com/paper/2212.12819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 53 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 16 of 16)

- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space](https://mlsanity.com/paper/2103.13726/) (2021)
- [Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks](https://mlsanity.com/paper/1904.04375/) (2019)
- [Multiple Access in Cellular V2X: Performance Analysis in Highly Congested Vehicular Networks](https://mlsanity.com/paper/1809.02678/) (2018)
- [Real-Time Hardware-In-the-Loop Emulation Framework for DSRC-based Connected Vehicle Applications](https://mlsanity.com/paper/1905.09267/) (2019)
- [V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures](https://mlsanity.com/paper/1903.01576/) (2019)
- [AICP: Augmented Informative Cooperative Perception](https://mlsanity.com/paper/2101.05508/) (2021)
- [Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2111.07162/) (2021)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control](https://mlsanity.com/paper/2203.15772/) (2022)
- [Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application](https://mlsanity.com/paper/1808.06463/) (2018)
- [Representing Realistic Human Driver Behaviors using a Finite Size Gaussian Process Kernel Bank](https://mlsanity.com/paper/2003.13837/) (2020)
- [Impact of Communication Loss on MPC based Cooperative Adaptive Cruise Control and Platooning](https://mlsanity.com/paper/2106.09094/) (2021)
- [Towards Learning Generalizable Driving Policies from Restricted Latent Representations](https://mlsanity.com/paper/2111.03688/) (2021)
- [Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms](https://mlsanity.com/paper/2208.05540/) (2022)

## Abstract (arXiv)

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.