Human Driver Behavior Prediction based on UrbanFlow (1911.03801v1)

Abstract: How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers' behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in urban intersection scenarios. Meanwhile, in order to collect human drivers' driving behavior data in the urban scenario, we describe a system called UrbanFlow which includes the whole procedure from raw bird's-eye view data collection via drone to the final processed trajectories. The system is mainly intended for urban scenarios but can be extended to be used for any traffic scenarios.

• The paper presents a novel LSTM-based framework leveraging the UrbanFlow dataset to predict human driver intentions and trajectories at urban intersections.
• It details an end-to-end data processing pipeline, including video stabilization, RetinaNet detection, and Kalman filtering, to ensure high-quality trajectory data.
• The research demonstrates improved prediction accuracy by integrating intention analysis, significantly reducing mean squared error for autonomous driving decisions.

Overview of "Human Driver Behavior Prediction based on UrbanFlow"

In the paper titled "Human Driver Behavior Prediction based on UrbanFlow," the authors propose an innovative approach to enhancing decision-making in autonomous vehicles (AVs) by predicting human driver behavior, specifically within urban intersection environments. The methodology employs a Long Short-Term Memory (LSTM)-based trajectory prediction framework and a comprehensive traffic data collection system called UrbanFlow, which utilizes bird's-eye view data gathered via drones. This approach directly tackles the complexities involved in modeling human driver interactions, thereby facilitating safer and more efficient automated driving experiences.

Methodology and Key Contributions

The paper is structured around three pivotal contributions:

1. UrbanFlow Dataset Collection: The authors developed an end-to-end traffic data collection pipeline capable of capturing comprehensive vehicle trajectory data from urban intersections via drone-based aerial observation. Compared to existing datasets, UrbanFlow improves upon earlier works like NGSIM and highD by focusing specifically on urban intersections, an environment prone to frequent and complex vehicle interactions.
2. LSTM-based Prediction Models: The paper presents LSTM models designed to predict both the intentions and trajectories of human drivers. The models leverage historical trajectory data to foresee whether a driver will go straight, turn left, or turn right when approaching an intersection. A secondary yield intention prediction component aims to resolve the sequence in which interacting vehicles will traverse a shared space, a critical component for avoiding collisions.
3. Data Processing and Analysis: The authors provided detailed methods for processing raw video footage into usable datasets, including video stabilization, vehicle detection using RetinaNet, and trajectory smoothing through Kalman filters. This rigorous data processing ensures high-quality inputs for the predictive models.

Results and Numerical Analysis

The experimental findings demonstrate the efficacy of the UrbanFlow system and accompanying predictive models. The proposed video stabilization method, which combines feature-based and homography-based alignment techniques, successfully optimizes the processing time (achieving efficiency improvements based on down-sampled data) while maintaining similarity integrity via structural similarity indices (SSIM).

In the prediction domain, integrating intention analysis into trajectory prediction markedly improved accuracy. Specifically, trajectory predictions augmented with direction and yield intention awareness, when benchmarked against generic LSTM methods, displayed significantly reduced mean squared error (MSE).

Implications and Future Directions

The implications of this research are particularly relevant to urban mobility—providing insights into human driver behavior at complex intersections can enable AVs to make more informed, timely, and safe navigational decisions. The approach enhances AVs' adaptability in shared driving environments, facilitating harmonious co-existence with human drivers.

Looking ahead, expanding the UrbanFlow dataset is crucial. Incorporating diverse urban scenarios such as T-intersections, stop-sign-controlled intersections, and others will enrich the data pool, enabling the development of more generalized models. Advancements in computational models, potentially incorporating reinforcement learning or hybrid architectures, might further refine predictive capabilities.

In conclusion, this research lays the groundwork for systematic understanding and prediction of human driving behaviors, representing a significant step in advancing the technology necessary for safer autonomous urban driving integration.