A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environment

TL;DR

This paper presents a novel cognitive-inspired trajectory prediction model for autonomous vehicles in mixed traffic environments, demonstrating significant performance improvements and robustness over existing methods, especially in complex and data-limited scenarios.

Contribution

It introduces a new model that integrates cognitive insights into trajectory prediction, enhancing interaction analysis and decision-making in mixed autonomy traffic.

Findings

Achieves 16.2% improvement on NGSIM dataset

Surpasses benchmarks on HighD and MoCAD datasets

Demonstrates robustness in data-limited and corner case scenarios

Abstract

As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional approaches, our model excels in analyzing interactions and behavior patterns in mixed autonomy traffic scenarios. It represents a significant leap forward, achieving marked performance improvements on several key datasets. Specifically, it surpasses existing benchmarks with gains of 16.2% on the Next Generation Simulation (NGSIM), 27.4% on the Highway Drone (HighD), and 19.8% on the Macao Connected Autonomous Driving (MoCAD) dataset. Our proposed model shows exceptional proficiency in handling corner cases, essential for real-world applications. Moreover, its robustness is evident in scenarios with missing or limited data, outperforming most of the state-of-the-art baselines. This adaptability and resilience position our model as a viable tool for real-world autonomous driving systems, heralding a new standard in vehicle trajectory prediction for enhanced safety and efficiency.