Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons

• URL: http://arxiv.org/abs/2412.09835v1
• Date: Fri, 13 Dec 2024 03:56:40 GMT
• Title: Which cycling environment appears safer? Learning cycling safety perceptions from pairwise image comparisons
• Authors: Miguel Costa, Manuel Marques, Carlos Lima Azevedo, Felix Wilhelm Siebert, Filipe Moura,
• Abstract summary: Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. In this study, we tackle the problem of capturing and understanding how individuals perceive cycling risk. We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments. We ask them to select the one they perceive as safer for cycling, if any. Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals' responses, learns preferences directly from images,
• Score: 2.3900828891729784
• License:
• Abstract: Cycling is critical for cities to transition to more sustainable transport modes. Yet, safety concerns remain a critical deterrent for individuals to cycle. If individuals perceive an environment as unsafe for cycling, it is likely that they will prefer other means of transportation. Yet, capturing and understanding how individuals perceive cycling risk is complex and often slow, with researchers defaulting to traditional surveys and in-loco interviews. In this study, we tackle this problem. We base our approach on using pairwise comparisons of real-world images, repeatedly presenting respondents with pairs of road environments and asking them to select the one they perceive as safer for cycling, if any. Using the collected data, we train a siamese-convolutional neural network using a multi-loss framework that learns from individuals' responses, learns preferences directly from images, and includes ties (often discarded in the literature). Effectively, this model learns to predict human-style perceptions, evaluating which cycling environments are perceived as safer. Our model achieves good results, showcasing this approach has a real-life impact, such as improving interventions' effectiveness. Furthermore, it facilitates the continuous assessment of changing cycling environments, permitting short-term evaluations of measures to enhance perceived cycling safety. Finally, our method can be efficiently deployed in different locations with a growing number of openly available street-view images.

Related papers

• DAVE: Diverse Atomic Visual Elements Dataset with High Representation of Vulnerable Road Users in Complex and Unpredictable Environments [60.69159598130235]
  We present a new dataset, DAVE, designed for evaluating perception methods with high representation of Vulnerable Road Users (VRUs) DAVE is a manually annotated dataset encompassing 16 diverse actor categories (spanning animals, humans, vehicles, etc.) and 16 action types (complex and rare cases like cut-ins, zigzag movement, U-turn, etc.) Our experiments show that existing methods suffer degradation in performance when evaluated on DAVE, highlighting its benefit for future video recognition research.
  arXiv  Detail & Related papers  (2024-12-28T06:13:44Z)
• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• Discovering Cyclists' Visual Preferences Through Shared Bike Trajectories and Street View Images Using Inverse Reinforcement Learning [2.678595263943329]
  We propose a novel framework aimed to quantify and interpret cyclists' complicated visual preferences. We adapt MEDIRL model for efficient estimation of cycling reward function. We find that cyclists focus on specific street visual elements when making route decisions.
  arXiv  Detail & Related papers  (2024-09-05T00:54:48Z)
• AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing [4.599618895656792]
  We propose a deep learning framework to support accurate, fast, and large-scale cycling stress assessments. Our framework features i) a contrastive learning approach that leverages the ordinal relationship among cycling stress labels, and ii) a post-processing technique that enforces spatial smoothness into our predictions.
  arXiv  Detail & Related papers  (2023-08-15T05:51:25Z)
• Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons [0.9299655616863538]
  This study presents a novel approach to identifying how the perception of cycling safety can be analyzed and understood. We repeatedly show respondents two road environments and ask them to select the one they perceive as safer for cycling. We compare several methods capable of rating cycling environments from pairwise comparisons and classify cycling environments perceived as safe or unsafe.
  arXiv  Detail & Related papers  (2023-07-25T10:31:45Z)
• Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios [147.16925581385576]
  We show how imitation learning combined with reinforcement learning can substantially improve the safety and reliability of driving policies. We train a policy on over 100k miles of urban driving data, and measure its effectiveness in test scenarios grouped by different levels of collision likelihood.
  arXiv  Detail & Related papers  (2022-12-21T23:59:33Z)
• Estimating city-wide hourly bicycle flow using a hybrid LSTM MDN [0.0]
  Efforts to increase the bicycle's mode-share involve many measures, one of them being the improvement of cycling safety. meaningful analysis of cycling safety requires accurate bicycle flow data that is generally sparse or not even available at a segment level. This paper presents a Deep Learning based approach, to estimate hourly bicycle flow in Copenhagen, conditional on weather, temporal and road conditions.
  arXiv  Detail & Related papers  (2022-04-20T17:00:29Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning [1.462434043267217]
  CyclingNet is a deep computer vision model based on convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities.
  arXiv  Detail & Related papers  (2021-01-31T23:59:28Z)
• LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving [139.33800431159446]
  LookOut is an approach to jointly perceive the environment and predict a diverse set of futures from sensor data. We show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset.
  arXiv  Detail & Related papers  (2021-01-16T23:19:22Z)

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.