Sensors and Sensing for Intelligent Vehicles

Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.

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[2]  Yi Sun,et al.  Multi-Stage Hough Space Calculation for Lane Markings Detection via IMU and Vision Fusion , 2019, Sensors.

[3]  Masatoshi Ishikawa,et al.  Real-Time Traffic Light Detection with Frequency Patterns Using a High-Speed Camera , 2020, Sensors.

[4]  Miguel Angel Sotelo,et al.  Assistive Intelligent Transportation Systems: The Need for User Localization and Anonymous Disability Identification , 2017, IEEE Intelligent Transportation Systems Magazine.

[5]  Oluremi Olatunbosun,et al.  A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations , 2019, Sensors.

[6]  Danchen Zhao,et al.  Simulating Dynamic Driving Behavior in Simulation Test for Unmanned Vehicles via Multi-Sensor Data , 2019, Sensors.

[7]  Quan Yuan,et al.  Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles , 2019, Sensors.

[8]  Ignacio Parra,et al.  High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps , 2018, Journal of Advanced Transportation.

[9]  Loraine Navarro,et al.  Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry , 2020, Sensors.

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[11]  Shiping Song,et al.  Motion State Estimation of Target Vehicle under Unknown Time-Varying Noises Based on Improved Square-Root Cubature Kalman Filter , 2020, Sensors.

[12]  Jahng Hyon Park,et al.  Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation , 2020, Sensors.

[13]  Minjin Baek,et al.  Vehicle Trajectory Prediction and Collision Warning via Fusion of Multisensors and Wireless Vehicular Communications , 2020, Sensors.

[14]  Le Minh Kieu,et al.  Deep learning methods in transportation domain: a review , 2018, IET Intelligent Transport Systems.

[15]  Yeun-Sub Byun,et al.  Sensor Fault Detection and Signal Restoration in Intelligent Vehicles , 2019, Sensors.

[16]  Chang Wang,et al.  Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic , 2020, Sensors.

[17]  Dariu Gavrila,et al.  EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Weiwen Deng,et al.  Research on a Simulation Method of the Millimeter Wave Radar Virtual Test Environment for Intelligent Driving , 2020, Sensors.

[19]  Hesham A Rakha,et al.  A Repeated Game Freeway Lane Changing Model , 2020, Sensors.

[20]  Efren Diez-Jimenez,et al.  Personal Rapid Transport System Compatible With Current Railways and Metros Infrastructure , 2020 .

[21]  Dong Yang,et al.  Combined Edge- and Stixel-based Object Detection in 3D Point Cloud , 2019, Sensors.

[22]  David Fernández Llorca.  Future trends of ITS in difficult times: A message from the new Editor-in-Chief of IET Intelligent Transport Systems , 2020 .

[23]  Asier Zubizarreta,et al.  A Fail-Operational Control Architecture Approach and Dead-Reckoning Strategy in Case of Positioning Failures , 2020, Sensors.

[24]  Dariu Gavrila,et al.  Pedestrian Detection from a Moving Vehicle , 2000, ECCV.

[25]  Zhuoping Yu,et al.  Reinforcement Learning-Based End-to-End Parking for Automatic Parking System , 2019, Sensors.

[26]  Manuel José Ibarra-Arenado,et al.  Shadow Detection in Still Road Images Using Chrominance Properties of Shadows and Spectral Power Distribution of the Illumination , 2020, Sensors.

[27]  David Fernández Llorca,et al.  Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles , 2020, Sensors.

[28]  Zhuoping Yu,et al.  Multi-sensor Fusion Road Friction Coefficient Estimation During Steering with Lyapunov Method , 2019, Sensors.

[29]  Luis Hernández-Martínez,et al.  Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach , 2020, Sensors.

[30]  Matthew Doude,et al.  Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving , 2019, Sensors.

[31]  Ryo Yanase,et al.  Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving , 2020, Sensors.

[32]  Xingcheng Wang,et al.  Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation , 2020, Sensors.

[33]  Lucia Pallottino,et al.  LiDAR-Based GNSS Denied Localization for Autonomous Racing Cars , 2020, Sensors.

[34]  Amith Khandakar,et al.  Portable System for Monitoring and Controlling Driver Behavior and the Use of a Mobile Phone While Driving , 2019, Sensors.

[35]  Jose M. Armingol,et al.  Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS , 2020, Sensors.

[36]  Ignacio Parra,et al.  3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns for road scene interpretation , 2020, 2020 IEEE Intelligent Vehicles Symposium (IV).

[37]  Myoungho Sunwoo,et al.  Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions , 2019, Sensors.

[38]  Weiwei Zhang,et al.  Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy , 2019, Sensors.

[39]  Myoungho Sunwoo,et al.  Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles , 2019, Sensors.

[40]  Jaewoo Yoon,et al.  Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter , 2020, Sensors.

[41]  Ignacio Parra,et al.  Combination of Feature Extraction Methods for SVM Pedestrian Detection , 2007, IEEE Transactions on Intelligent Transportation Systems.

[42]  André L. L. de Aquino,et al.  Vehicle Driver Monitoring through the Statistical Process Control , 2019, Sensors.

[43]  Franz Kummert,et al.  Spatial ray features for real-time ego-lane extraction , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[44]  Xianfeng Yuan,et al.  Real-Time Photometric Calibrated Monocular Direct Visual SLAM , 2019, Sensors.