# On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

arXiv: https://arxiv.org/abs/2112.00942 · HTML: https://mlsanity.com/paper/2112.00942/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale](https://mlsanity.com/paper/1909.04422/) (2019)
- [Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference](https://mlsanity.com/paper/2003.02425/) (2020)
- [Looking at the right stuff: Guided semantic-gaze for autonomous driving](https://mlsanity.com/paper/1911.10455/) (2019)
- [Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss](https://mlsanity.com/paper/2011.06679/) (2020)
- [Explaining Autonomous Driving by Learning End-to-End Visual Attention](https://mlsanity.com/paper/2006.03347/) (2020)
- [No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs](https://mlsanity.com/paper/1802.08755/) (2018)
- [Interaction Graphs for Object Importance Estimation in On-road Driving Videos](https://mlsanity.com/paper/2003.06045/) (2020)
- [Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics](https://mlsanity.com/paper/1908.11262/) (2019)
- [Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data](https://mlsanity.com/paper/2104.11489/) (2021)
- [Behavioral Research and Practical Models of Drivers' Attention](https://mlsanity.com/paper/2104.05677/) (2021)
- [Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges](https://mlsanity.com/paper/2006.09181/) (2020)
- [Exploring Driving-aware Salient Object Detection via Knowledge Transfer](https://mlsanity.com/paper/2105.08286/) (2021)

## Built on by (most cited first, 13 of 13)

- [Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets](https://mlsanity.com/paper/2402.07320/) (2024)
- [The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration](https://mlsanity.com/paper/2401.16634/) (2024)
- [ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2402.03235/) (2024)
- [Language-Driven Active Learning for Diverse Open-Set 3D Object Detection](https://mlsanity.com/paper/2404.12856/) (2024)
- [Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context](https://mlsanity.com/paper/2301.05804/) (2023)
- [Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations](https://mlsanity.com/paper/2305.04516/) (2023)
- [Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis](https://mlsanity.com/paper/2305.04506/) (2023)
- [Can Vision-Language Models Understand and Interpret Dynamic Gestures from Pedestrians? Pilot Datasets and Exploration Towards Instructive Nonverbal Commands for Cooperative Autonomous Vehicles](https://mlsanity.com/paper/2504.10873/) (2025)
- [Evaluating Vision-Language Models for Zero-Shot Detection, Classification, and Association of Motorcycles, Passengers, and Helmets](https://mlsanity.com/paper/2408.02244/) (2024)
- [Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving](https://mlsanity.com/paper/2405.09049/) (2024)
- [What Do They See? Interpreting Complex Road Scenarios Through the Eyes of Vision-Language-Action Models for Safe and Trustworthy Autonomous Vehicle Learning](https://mlsanity.com/paper/2607.16938/) (2026)
- [A Scale-Invariant Trajectory Simplification Method for Efficient Data Collection in Videos](https://mlsanity.com/paper/2304.01340/) (2023)
- [Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations](https://mlsanity.com/paper/2307.14571/) (2023)

## Abstract (arXiv)

Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents. While all static scene elements are a source of information, there is asymmetric importance to the information available to the ego vehicle. We present a dataset with a novel feature, sign salience, defined to indicate whether a sign is distinctly informative to the goals of the ego vehicle with regards to traffic regulations. Using convolutional networks on cropped signs, in tandem with experimental augmentation by road type, image coordinates, and planned maneuver, we predict the sign salience property with 76% accuracy, finding the best improvement using information on vehicle maneuver with sign images.