Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning

By Vasili Ramanishka, Yi-Ting Chen, Teruhisa Misu, Kate Saenko. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

339 citations recorded by the source corpus.

Driving Scene understanding is a key ingredient for intelligent transportation systems. To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes. We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. We provide a detailed analysis of HDD with a comparison to other driving datasets. A novel annotation methodology is introduced to enable research on driver behavior understanding from untrimmed data sequences. As the first step, baseline algorithms for driver behavior detection are trained and tested to demonstrate the feasibility of the proposed task.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7b5bd8535e49747b752c5eb8c0d80cd45a30078b

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Citations in the corpus

The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes (2019, background): “dataset1 [15], a large scale naturalistic driving dataset collected in San Francisco Bay Area.”
A Survey of End-to-End Driving: Architectures and Training Methods (2020, background): “0 HDD [144] 3 X X X X X X X X X 104h Academic Brain4Cars [145] 1 X X X X 1180 miles Academic Li-Vi [95], [146] 1 X X X X X 10h Academic DDD17 [147] 1* X X X X X X 12h CC-BY-NC-SA-4.”
Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, neutral): “HDD [117] 2018 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 104h San Francisco Academic”
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps (2019, methodology): “One includes motion data of surrounding entities from onboard LiDARs and front-view cameras, such as Argoverse [19] and HDD dataset [24].”
Exploring applications of deep reinforcement learning for real-world autonomous driving systems (2019, methodology): “Berkeley Driving Dataset (Xu et al., 2017) Learn driving policy from demonstrations Baidu’s ApolloScape Multiple sensors & Driver Behaviour Proﬁles Honda Driving Dataset (Ramanishka et al., 2018) 4 level annotation (stimulus, action, cause, and attention objects) for driver behavior proﬁle.”
Explanations in Autonomous Driving: A Survey (2021, background): “HDD [112] 374,400s 3 3 What explanations for driver actions Class B”
nuScenes: A Multimodal Dataset for Autonomous Driving (2019, background): “velocities, accelerations, torque, steering angles, wheel speeds) similar to [65].”