HetroD: A High-Fidelity Drone Dataset and Benchmark for Autonomous Driving in Heterogeneous Traffic
This addresses the critical challenge of developing autonomous vehicles for real-world heterogeneous traffic dominated by vulnerable road users, which is incremental as it provides a new dataset and benchmark.
The authors tackled the problem of autonomous driving in heterogeneous traffic environments with vulnerable road users by creating HetroD, a large-scale drone dataset with 65.4k high-fidelity agent trajectories (70% from VRUs) and benchmarks, revealing that state-of-the-art models struggle with lateral VRU movements and unstructured maneuvers.
We present HetroD, a dataset and benchmark for developing autonomous driving systems in heterogeneous environments. HetroD targets the critical challenge of navi- gating real-world heterogeneous traffic dominated by vulner- able road users (VRUs), including pedestrians, cyclists, and motorcyclists that interact with vehicles. These mixed agent types exhibit complex behaviors such as hook turns, lane splitting, and informal right-of-way negotiation. Such behaviors pose significant challenges for autonomous vehicles but remain underrepresented in existing datasets focused on structured, lane-disciplined traffic. To bridge the gap, we collect a large- scale drone-based dataset to provide a holistic observation of traffic scenes with centimeter-accurate annotations, HD maps, and traffic signal states. We further develop a modular toolkit for extracting per-agent scenarios to support downstream task development. In total, the dataset comprises over 65.4k high- fidelity agent trajectories, 70% of which are from VRUs. HetroD supports modeling of VRU behaviors in dense, het- erogeneous traffic and provides standardized benchmarks for forecasting, planning, and simulation tasks. Evaluation results reveal that state-of-the-art prediction and planning models struggle with the challenges presented by our dataset: they fail to predict lateral VRU movements, cannot handle unstructured maneuvers, and exhibit limited performance in dense and multi-agent scenarios, highlighting the need for more robust approaches to heterogeneous traffic. See our project page for more examples: https://hetroddata.github.io/HetroD/