AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control
Not on the map. Why?
Abstract (arXiv)
Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, Smart Traffic Control and Driver Modelling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, the availability of data from different stakeholders is need. Further, though AI technologies provide accurate predictions and classifications there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability, while the models have difficulties explaining how they come to a certain conclusion it is difficult for humans to trust it.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 45 of its 148 references that are in the map. First posted on arXiv 7 Apr 2021.
Builds on
45 of 148 references.
References · 45
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Mask R-CNNcited by 7,259
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
- 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
- 2015SSD: Single Shot MultiBox Detectorcited by 3,125
- 2016Pyramid Scene Parsing Networkcited by 2,472
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
33 more
- 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
- 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
- 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
- 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
- 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
- 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
- 2018Deep Learning for Generic Object Detection: A Surveycited by 392
- 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
- 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 313
- 2018Deep Facial Expression Recognition: A Surveycited by 269
- 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
- 2019Human Motion Trajectory Prediction: A Surveycited by 231
- 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
- 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
- 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
- 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
- 2017PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimationcited by 170
- 2018Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flowcited by 135
- 2019Fast-SCNN: Fast Semantic Segmentation Networkcited by 124
- 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
- 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 99
- 2018Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detectioncited by 82
- 2019Attention Guided Graph Convolutional Networks for Relation Extractioncited by 82
- 2018Self-Supervised Model Adaptation for Multimodal Semantic Segmentationcited by 73
- 2017HARP: Hierarchical Representation Learning for Networkscited by 58
- 2019SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Drivingcited by 57
- 2014Deformable Part Models are Convolutional Neural Networkscited by 35
- 2017Choosing Smartly: Adaptive Multimodal Fusion for Object Detection in Changing Environmentscited by 34
- 2018N-ary Relation Extraction using Graph State LSTMcited by 26
- 2018A Survey on Periocular Biometrics Researchcited by 20
- 2018Action and intention recognition of pedestrians in urban trafficcited by 10
- 2018Expression Recognition Using the Periocular Region: A Feasibility Studycited by 3
- 2018Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learningcited by 1
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.
2 papers.