TLDR: HiD2 is a novel framework that generates realistic, high-density, and diverse traffic scenarios for autonomous driving. It addresses the “long-tail problem” in existing datasets by creating complex interactions like lane changes, overtaking, and turns on real-world maps. By converting continuous road environments into a structured grid and employing behavior-aware generation mechanisms, HiD2 synthesizes dynamically feasible and collision-free trajectories. This synthetic data significantly improves the robustness and generalization of trajectory prediction models, particularly in challenging, crowded, and interaction-heavy environments, thereby enhancing the safety and performance of autonomous vehicles.
Autonomous driving systems rely heavily on accurate trajectory prediction to ensure safety and smooth navigation in complex environments. However, a significant challenge in developing these systems stems from the limitations of existing datasets. Most benchmark datasets suffer from what is known as the “long-tail distribution problem.” This means they primarily feature low-density traffic scenarios and simple driving behaviors, while high-density situations and critical maneuvers like lane changes, overtaking, and sharp turns are severely underrepresented.
This imbalance in data hinders the ability of prediction models to generalize effectively, leading to overly optimistic evaluations. Models might appear competent in straightforward situations but fail when faced with rare, yet safety-critical, scenarios. To tackle this fundamental data bottleneck, researchers have introduced HiD2, a novel trajectory generation framework designed to create high-density traffic and diverse agent-interaction scenarios.
Addressing the Data Gap with HiD2
HiD2 stands for “High-Density and Diverse” trajectory generation. Unlike previous approaches that either rely on extensive manual effort in simulators or merely recycle existing patterns from limited datasets, HiD2 synthesizes new, realistic scenarios directly within real-world maps. This framework aims to simultaneously boost scenario density and enrich the variety of driving behaviors.
The core of HiD2 involves converting continuous road environments into a structured grid representation. This grid allows for precise path planning, explicit detection of potential conflicts, and coordinated movements among multiple agents. Built upon this structured representation, HiD2 incorporates “behavior-aware generation mechanisms.” These mechanisms combine rule-based decision triggers with advanced trajectory smoothing techniques (Frenet-based) and dynamic feasibility constraints. This sophisticated design enables the system to generate realistic high-density scenarios and rare, complex interactions that are often missing in real-world data.
How HiD2 Works: A Closer Look
The process begins with a detailed analysis of high-definition (HD) maps, extracting lane topology, geometry, and connectivity. This continuous road information is then discretized into a grid, where each cell has semantic attributes and an occupancy state (free, occupied by original agents, or occupied by generated agents). Generated agents then follow specific policies—straight driving, left turn, right turn, lane change, or overtaking—which are triggered based on temporal conditions and spatial feasibility checks, such as the availability of adjacent lanes or safe gaps in traffic.
For instance, a lane change or overtaking maneuver is only initiated if specific conditions are met, ensuring that the generated trajectories are collision-free and dynamically feasible. The system also includes robust conflict resolution mechanisms to manage interactions between agents, prioritizing original agents and arbitrating conflicts among generated ones. Finally, the discrete grid paths are transformed into smooth, continuous trajectories using Frenet coordinates, which are then checked against curvature and lateral acceleration constraints to ensure physical realism and comfort.
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Impact and Benefits
Extensive experiments conducted on large-scale datasets like Argoverse 1 and Argoverse 2 demonstrate the significant impact of HiD2. The generated data not only maintains high statistical consistency with original datasets in terms of motion realism (longitudinal acceleration, lateral acceleration, jerk) but also ensures scenario-level safety (low collision and off-road rates).
Crucially, HiD2 effectively enriches the diversity of scenarios. It dramatically increases the proportion of high-density situations (e.g., scenarios with more than 40 agents) and complex driving behaviors, including lane changes, overtaking, and various turns, which are typically scarce. When state-of-the-art trajectory prediction models are augmented with HiD2-generated data, their performance significantly improves, especially in challenging high-density and interaction-heavy scenarios. This enhancement in model robustness is vital for the safe deployment of autonomous vehicles.
The research paper, available at arxiv.org/pdf/2510.02627, concludes that HiD2 provides a unified framework that addresses the long-tail problem in trajectory prediction by generating realistic and safe trajectories with rare, yet critical, behaviors directly on real maps. This approach offers a powerful solution for improving the generalization capabilities of autonomous driving systems in the most demanding traffic conditions.
