mlsanityData to 7 Oct 2026

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

arXiv 2101.02082 ↗First posted 6 Jan 2021cs.AI cs.HCcited by 3

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Abstract (arXiv)

As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles. In this context, the functionality inside the vehicle cabin plays a key role in ensuring a safe and pleasant journey for driver and passenger alike. At the same time, recent advances in the field of artificial intelligence (AI) have enabled a whole range of new applications and assistance systems to solve automated problems in the vehicle cabin. This paper presents a thorough survey on existing work that utilizes AI methods for use-cases inside the driving cabin, focusing, in particular, on application scenarios related to (1) driving safety and (2) driving comfort. Results from the surveyed works show that AI technology has a promising future in tackling in-cabin tasks within the autonomous driving aspect.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 83 references that are in the map. First posted on arXiv 6 Jan 2021.

Builds on

10 of 83 references.

References · 10

  1. 2017Predicting the Driver's Focus of Attention: the DR(eye)VE Projectcited by 79
  2. 2015Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecturecited by 42
  3. 2016Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecturecited by 31
  4. 2017Real-time Distracted Driver Posture Classificationcited by 29
  5. 2016POSEidon: Face-from-Depth for Driver Pose Estimationcited by 16
  6. 2020Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoringcited by 12
  7. 2019Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterancescited by 8
  8. 2019Real-Time Driver State Monitoring Using a CNN Based Spatio-Temporal Approachcited by 6
  9. 2017Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabinscited by 1
  10. 2017A Supervised Learning Concept for Reducing User Interaction in Passenger Carscited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.

  1. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  2. 2024Multi-modality action recognition based on dual feature shift in vehicle cabin monitoringcited by 8
  3. 2024MultiFuser: Multimodal Fusion Transformer for Enhanced Driver Action Recognitioncited by 4