# Sensing accident-prone features in urban scenes for proactive driving and accident prevention

arXiv: https://arxiv.org/abs/2202.12788 · HTML: https://mlsanity.com/paper/2202.12788/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 83 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning](https://mlsanity.com/paper/1611.05594/) (2016)
- [Feature relevance quantification in explainable AI: A causal problem](https://mlsanity.com/paper/1910.13413/) (2019)
- [Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs](https://mlsanity.com/paper/2008.02312/) (2020)
- [Sanity Checks for Saliency Metrics](https://mlsanity.com/paper/1912.01451/) (2019)
- [A Consistent and Efficient Evaluation Strategy for Attribution Methods](https://mlsanity.com/paper/2202.00449/) (2022)
- [The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](https://mlsanity.com/paper/1907.09294/) (2019)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models](https://mlsanity.com/paper/2001.11757/) (2020)
- [Building Instance Classification Using Street View Images](https://mlsanity.com/paper/1802.09026/) (2018)
- [Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution](https://mlsanity.com/paper/1905.01752/) (2019)
- [Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights](https://mlsanity.com/paper/1909.09638/) (2019)
- [Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development](https://mlsanity.com/paper/1911.08007/) (2019)
- [Identifying safe intersection design through unsupervised feature extraction from satellite imagery](https://mlsanity.com/paper/2010.15343/) (2020)
- [Explainable, automated urban interventions to improve pedestrian and vehicle safety](https://mlsanity.com/paper/2110.11672/) (2021)

## Built on by (most cited first, 4 of 4)

- [Domain-Enhanced Dual-Branch Model for Efficient and Interpretable Accident Anticipation](https://mlsanity.com/paper/2507.12755/) (2025)
- [Road Redesign Technique Achieving Enhanced Road Safety by Inpainting with a Diffusion Model](https://mlsanity.com/paper/2302.07440/) (2023)
- [PoseViNet: Distracted Driver Action Recognition Framework Using Multi-View Pose Estimation and Vision Transformer](https://mlsanity.com/paper/2312.14577/) (2023)
- [High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics](https://mlsanity.com/paper/2409.11206/) (2024)

## Abstract (arXiv)

In urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents due to missing these visual cues, this paper proposes a visual notification of AP-features to drivers based on real-time images obtained via dashcam. For this purpose, Google Street View images around accident hotspots (areas of dense accident occurrence) identified by a real-accident dataset are used to train a novel attention module to classify a given urban scene into an accident hotspot or a non-hotspot (area of sparse accident occurrence). The proposed module leverages channel, point, and spatial-wise attention learning on top of different CNN backbones. This leads to better classification results and more certain AP-features with better contextual knowledge when compared with CNN backbones alone. Our proposed module achieves up to 92% classification accuracy. The capability of detecting AP-features by the proposed model were analyzed by a comparative study of three different class activation map (CAM) methods, which were used to inspect specific AP-features causing the classification decision. Outputs of CAM methods were processed by an image processing pipeline to extract only the AP-features that are explainable to drivers and notified using a visual notification system. Range of experiments was performed to prove the efficacy and AP-features of the system. Ablation of the AP-features taking 9.61%, on average, of the total area in each image increased the chance of a given area to be classified as a non-hotspot by up to 21.8%.