Mining Personalized Climate Preferences for Assistant Driving

• URL: http://arxiv.org/abs/2006.08846v1
• Date: Tue, 16 Jun 2020 00:45:08 GMT
• Title: Mining Personalized Climate Preferences for Assistant Driving
• Authors: Feng Hu
• Abstract summary: We propose a novel approach for climate control, driver behavior recognition and driving recommendation for better fitting drivers' preferences in their daily driving. A prototype using a client-server architecture with an iOS app and an air-quality monitoring sensor has been developed. Real-world experiments on driving data of 11,370 km (320 hours) by different drivers in multiple cities worldwide have been conducted.
• Score: 1.6752182911522522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Both assistant driving and self-driving have attracted a great amount of attention in the last few years. However, the majority of research efforts focus on safe driving; few research has been conducted on in-vehicle climate control, or assistant driving based on travellers' personal habits or preferences. In this paper, we propose a novel approach for climate control, driver behavior recognition and driving recommendation for better fitting drivers' preferences in their daily driving. The algorithm consists three components: (1) A in-vehicle sensing and context feature enriching compnent with a Internet of Things (IoT) platform for collecting related environment, vehicle-running, and traffic parameters that affect drivers' behaviors. (2) A non-intrusive intelligent driver behaviour and vehicle status detection component, which can automatically label vehicle's status (open windows, turn on air condition, etc.), based on results of applying further feature extraction and machine learning algorithms. (3) A personalized driver habits learning and preference recommendation component for more healthy and comfortable experiences. A prototype using a client-server architecture with an iOS app and an air-quality monitoring sensor has been developed for collecting heterogeneous data and testing our algorithms. Real-world experiments on driving data of 11,370 km (320 hours) by different drivers in multiple cities worldwide have been conducted, which demonstrate the effective and accuracy of our approach.

Related papers

• Situation Awareness for Driver-Centric Driving Style Adaptation [3.568617847600189]
  We propose a situation-aware driving style model based on different visual feature encoders pretrained on fleet data. Our experiments show that the proposed method outperforms static driving styles significantly and forms plausible situation clusters.
  arXiv  Detail & Related papers  (2024-03-28T17:19:16Z)
• Automated Automotive Radar Calibration With Intelligent Vehicles [73.15674960230625]
  We present an approach for automated and geo-referenced calibration of automotive radar sensors. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner.
  arXiv  Detail & Related papers  (2023-06-23T07:01:10Z)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [75.83518507463226]
  Nonobjective driving experience is difficult to model. In this paper, we propose a FeedBack Loop Network (FBLNet) which attempts to model the driving experience accumulation procedure. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset [103.35624417260541]
  Decentralized vehicle coordination is useful in understructured road environments. We collect the Berkeley DeepDrive Drone dataset to study implicit "social etiquette" observed by nearby drivers. The dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and for computer vision in remote sensing settings.
  arXiv  Detail & Related papers  (2022-09-19T05:06:57Z)
• A Machine Learning Smartphone-based Sensing for Driver Behavior Classification [1.552282932199974]
  We propose to collect data sensors available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance.
  arXiv  Detail & Related papers  (2022-02-01T10:12:36Z)
• Driver2vec: Driver Identification from Automotive Data [44.84876493736275]
  Driver2vec is able to accurately identify the driver from a short 10-second interval of sensor data. Driver2vec is trained on a dataset of 51 drivers provided by Nervtech.
  arXiv  Detail & Related papers  (2021-02-10T03:09:13Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• Learning Accurate and Human-Like Driving using Semantic Maps and Attention [152.48143666881418]
  This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. We exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data.
  arXiv  Detail & Related papers  (2020-07-10T22:25:27Z)
• TripMD: Driving patterns investigation via Motif Analysis [3.42658286826597]
  TripMD is a system that extracts the most relevant driving patterns from sensor recordings. We show that our system can extract a rich number of driving patterns from a single driver.
  arXiv  Detail & Related papers  (2020-07-07T18:34:31Z)
• Improved YOLOv3 Object Classification in Intelligent Transportation System [29.002873450422083]
  An algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway. The model has a good performance and is robust to road blocking, different attitudes, and extreme lighting.
  arXiv  Detail & Related papers  (2020-04-08T11:45:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.