Honda Research Institute Driving Dataset

Updated 19 October 2025

Honda Research Institute Driving Dataset is an extensively annotated, multimodal dataset integrating visual, LiDAR, and CAN data to capture dynamic driving scenarios.
It employs a four-layer hierarchical annotation scheme that details goal-oriented actions, stimulus-driven responses, causal events, and attentional focus with high precision.
Fusion of sensor and annotation data enables advanced applications including action recognition, risk assessment, and anomaly detection in autonomous driving systems.

The Honda Research Institute Driving Dataset (HDD) is an extensively annotated, multimodal dataset developed for advancing research in driver behavior understanding, causal reasoning, and intelligent vehicle systems. HDD emphasizes the joint modeling of temporal, semantic, and attentional aspects of driving in real-world, heterogeneous environments, and provides a unique resource for tasks ranging from action recognition to risk assessment and adversarial anomaly detection.

1. Dataset Composition and Sensor Layout

HDD comprises 104 hours of real human driving data collected in the San Francisco Bay Area. The recordings encompass 137 driving sessions averaging 45 minutes each, traversing urban, suburban, and highway scenarios. Sensor modalities include:

Three Point Grey Grasshopper 3 cameras (1920×1200 px, 30 Hz, center FOV 80°, side FOV 90°)
Velodyne HDL-64E S2 3D LiDAR (10 Hz, 64 channels, 100 m range, 360° hFOV)
GeneSys Electronics Automotive Dynamic Motion Analyzer (gyroscope, accelerometer, GPS at 120 Hz)
CAN bus sensors: throttle angle, brake pressure, steering angle, yaw rate, and speed at 100 Hz

All sensors are synchronized via ROS on Ubuntu, with hardware timestamping to ensure temporal alignment. The dataset, in its processed form, occupies ~150 GB and covers a broad spectrum of driving and traffic interactions (Ramanishka et al., 2018).

2. Annotation Methodology

HDD introduces a four-layer hierarchical annotation scheme:

Goal-oriented actions: High-level maneuvers (left turn, right turn, U-turn, lane branch, merge)
Stimulus-driven actions: Reactive maneuvers in response to environmental changes (stops, deviations)
Cause: Semantic labels for event causes (congestion, traffic light, pedestrian, parked car)
Attention: Bounding box annotations indicating participants/objects attended by the driver

Annotations are executed using the ELAN toolkit, with multiple annotators, expert review, and 98% inter-annotator agreement (Ramanishka et al., 2018). This scheme unifies behavioral, causal, and attentional components, providing rich context for analyzing not merely what a driver does, but why.

3. Baselines and Algorithmic Benchmarks

For behavior detection, several LSTM-based baseline models were established:

Model	Input Modality	mAP Goal-oriented Actions
CNN pool	RGB video	Lower than CNN+Sensors
Sensors	CAN bus only	Lower than CNN+Sensors
CNN conv	CNN features (conv)	Lower than CNN+Sensors
CNN+Sensors	RGB + CAN fusion	~32.71%

These models utilize InceptionResnet-V2 features (dimensionally reduced via 1×1 convolution, e.g., 8×8×1536 → 8×8×20) fed to LSTMs (hidden size 2000) sampled at 3 Hz. Fusion of visual and dynamic (CAN) signals yields the best detection accuracy, highlighting modality complementarity (Ramanishka et al., 2018).

4. Applications in Learning, Recognition, and Risk Analysis

HDD supports a breadth of advanced research domains:

Action Recognition (Self-supervised Learning): A two-stream self-supervised framework employs spatial (center RGB) and motion ("stack of differences", SOD) towers for spatio-temporal alignment and sequence verification. The network, leveraging 70-frame clips, addresses long-tail class imbalance and intra-class variation, yielding mean accuracy improvements when fine-tuned (~82.78% for HDD, outperforming random initialization) (Taha et al., 2018).
Multi-modal Retrieval and Uncertainty Quantification: Conditional retrieval networks fuse camera and CAN signals, disentangle similarity notions (goal-oriented, stimulus-driven), and employ MC dropout for epistemic uncertainty. Quantitative evaluations demonstrate up to 6% mAP improvements in uncertain retrieval scenarios (Taha et al., 2019).
Driver-centric Risk Assessment and Object Identification: Causal inference frameworks leverage HDD's annotations to disentangle risk factors, compute average causal effect (ACE), and outperform baseline object detectors in identifying objects that induce risky maneuvers (Li et al., 2020). DROID, a two-stage framework, models scene-object interactions via ego-thing graphs and intervention-based risk assessment using changes in the driver "Go" score (Li et al., 2021).
Anomaly and Attack Detection: Sensor fusion frameworks utilize LSTM (location shift prediction), k-NN/DTW (turn classification), and adaptive DBSCAN (real-time thresholding via recursive mean and standard deviation) to robustly detect GNSS/GPS spoofing attacks (e.g., turn-by-turn, stop, overshoot, small bias). Detection accuracies consistently exceed 98%, with low latency and resilience to gradual and sophisticated adversarial patterns, validated across five clean and attacked HDD subsets (Dasgupta et al., 2021, Dasgupta et al., 2021, Mohammadi et al., 12 Oct 2025).

5. Comparative Analysis and Contributions

HDD stands out relative to other datasets (KITTI, Cityscapes, Oxford RobotCar, comma.ai, BDD-Nexar) for its focus on behavioral and causal reasoning rather than mere object detection/segmentation. It provides a unique blend of visual, LiDAR, and CAN signals, together with high-fidelity, temporally-resolved annotations for downstream learning (Ramanishka et al., 2018). Its structure enables multi-task and multi-modal learning paradigms, sensor fusion, and scene understanding in dynamic environments.

6. Challenges, Limitations, and Future Research

Significant open problems include:

Class Imbalance and Rare Event Modeling: Many driving events (e.g., "merge", "right lane branch") are rare, with a skewed distribution requiring robust bias mitigation during learning. Self-supervised approaches show promise, but additional techniques for data balancing and rare event augmentation are warranted (Taha et al., 2018).
Overfitting and Generalization: High-capacity models (e.g., with ImageNet pretraining) overfit rapidly when fine-tuned on HDD subsets, requiring stronger regularization, augmentation, or domain adaptation strategies (Taha et al., 2018).
Fusion and Epistemic Uncertainty: Handling missing modalities, noisy sensor data, and uncertain action signals remains a challenge. MC dropout and adaptive fusion strategies are effective but further advances in cross-modal Bayesian modeling are needed (Taha et al., 2019).
Adaptive and Reinforcement-based Attack Detection: While adaptive DBSCAN achieves high spoofing detection accuracy, future directions include deep reinforcement learning for threshold optimization, continual adaptation to evolving attack patterns, and extended validation with additional sensor fusion modalities (Mohammadi et al., 12 Oct 2025).
Explaining Driver Decision-making: The DROID framework and causal inference models open the path to explainable, driver-centric risk assessment, but finer-grained modeling of scene object interactions and intervention effects, particularly for ambiguous or occluded scenes, remain open (Li et al., 2021).

7. Implications for Intelligent Transportation Systems

HDD’s comprehensive sensor suite, annotation depth, and real-world variability create a foundation for intelligent transportation research:

Autonomous Driving: Building decision systems that rationalize not merely actions, but underlying causes and attention, allowing for human-like planning and risk mitigation.
Driver Assistance: Enabling anticipatory systems that recognize driver intent, explain why interventions occur, and adapt feedback based on context and risk profile.
Cybersecurity: Enhancing navigation robustness against adversarial sensor attacks (spoofing, signal manipulation) using multi-modal fusion and anomaly detection frameworks.
Policy and Regulatory Analysis: Providing quantitative causal evidence for designing safety protocols, regulatory benchmarks, and insurance risk models based on real driver behavior.

HDD will continue to fuel progress in robust perception, reasoning, and decision-making for next-generation automotive and traffic systems, serving as a benchmark for algorithmic and methodological development across interdisciplinary research in autonomous vehicles, human factors, and transportation safety.