## 4 Dataset Observation and Prepossessing
### Dataset Observation  
Various trajectory datasets exist for road users, but very few have met the needs of autonomous driving research. Examples include BIWI Walking Pedestrians [20], Stanford Drone [27], CITR and DUT [28], highD [29], Interaction [30], and Ko-PER [31]. Most lack sufficient data or public road scenarios. The inD dataset [32] is a new and unique dataset of naturalistic vehicle trajectories recorded at German intersections using a drone. The dataset includes more than 11,500 road users, including vehicles, bicyclists, and pedestrians, at four different locations (Braunschweig, Frankfurt, Lindau, Wurzburg) with varying traffic density and complexity. Table 3 shows a comparison of the inD dataset with other datasets [32].  
The _inD_ dataset surpasses other datasets in capturing naturalistic road user behavior due to its strategic design principles:  
* **Preserving naturalistic behavior:** By avoiding visible sensors resembling traffic surveillance cameras, the _inD_ dataset ensures road users are not impacted by the measurement method.
* **Having satisfactory size:** The inclusion of trajectories from thousands of road users provides the necessary size and variety for robust data-driven algorithms.
* **Differentiating recording locations and times:** The _inD_ dataset captures measurements from multiple sites and diverse times of day, including public roads, ensuring the coverage of different road layouts, densities, and traffic rules for enhanced significance in automated driving scenarios.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Title** & **Location** & **\# Trajectories** & **\# Locations** & **Road User Types** & \begin{tabular}{c} **Data** \\ **Frequency** \\ \end{tabular} & **Method** \\ \hline BIWI ETH & \begin{tabular}{l} University building \\ entry \\ \end{tabular} & 360 & 1 & pedestrian & 2.5 Hz & stat. sensor \\ \hline Interaction & Urban intersection & 18,642 & 4 & vehicles & 10 Hz & drone/cam. \\ \hline Ko-PER & Urban intersection & 350 & 1 & pedestrian, bicycle, car, truck & 25 Hz & stat. sensor \\ \hline Crowds UCY/ & \begin{tabular}{l} Campus, urban \\ street \\ \end{tabular} & 909 & 3 & pedestrian & 2.5 Hz & stat. sensor \\ \hline Stanford Drone & Campus & 10,240 & 8 & pedestrian, bicycle, car, skateboard, cart, bus & 25 Hz & drone \\ \hline BIWI & \begin{tabular}{l} Hotel sidewalk, \\ hotel entry \\ \end{tabular} & 389 & 1 & pedestrian & 2.5 Hz & stat. sensor \\ \hline VRU Trajectory & Urban intersection & 3278 & 1 & pedestrian, bicycle & 25 Hz & stat. sensor \\ \hline DUT & Campus & 1862 & 2 & pedestrian, vehicles & 23.98 Hz & drone \\ \hline CITR & \
\begin{tabular}{l} Designed \\ experiment \\ \end{tabular} & 340 & 1 & pedestrian, golf-cart & 29.97 Hz & drone \\ \hline inD & Urban intersection & 11,500 & 4 &
\begin{tabular}{l} pedestrian, bicycle, car, truck, bus \\ \end{tabular} & 25 Hz & drone \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of trajectory data and characteristics.  
* **Detecting and tracking all road user types:** Unlike datasets that limit themselves to specific categories, the \(inD\) dataset tracks all road users and recognizes the vital importance of capturing interactions across diverse user types.
* **Tracking road users with top accuracy:** The \(inD\) dataset guarantees trajectories with a positioning inaccuracy of less than 0.1 m, regardless of road user type, ensuring precision in capturing the intricacies of user movement.
* **Including infrastructure details:** With the precise recording of road layouts and local traffic rules, the \(inD\) dataset acknowledges the dependency of road user behavior on these factors, providing comprehensive information within the dataset.