# Object criticality for safer navigation

arXiv: https://arxiv.org/abs/2406.10232 · HTML: https://mlsanity.com/paper/2406.10232/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation](https://mlsanity.com/paper/2105.08919/) (2021)
- [A General Framework for Uncertainty Estimation in Deep Learning](https://mlsanity.com/paper/1907.06890/) (2019)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection](https://mlsanity.com/paper/1804.05132/) (2018)
- [SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds](https://mlsanity.com/paper/2004.02774/) (2020)
- [Learning to Evaluate Perception Models Using Planner-Centric Metrics](https://mlsanity.com/paper/2004.08745/) (2020)
- [Learning an Uncertainty-Aware Object Detector for Autonomous Driving](https://mlsanity.com/paper/1910.11375/) (2019)
- [On Offline Evaluation of 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2308.12779/) (2023)
- [Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles](https://mlsanity.com/paper/2106.04146/) (2021)
- [Safety-Aware Hardening of 3D Object Detection Neural Network Systems](https://mlsanity.com/paper/2003.11242/) (2020)
- [Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures](https://mlsanity.com/paper/2203.02205/) (2022)
- [Interaction-Dynamics-Aware Perception Zones for Obstacle Detection Safety Evaluation](https://mlsanity.com/paper/2206.12471/) (2022)
- [Verifiable Obstacle Detection](https://mlsanity.com/paper/2208.14403/) (2022)

## Built on by (most cited first, 1 of 1)

- [Contour Errors: Ego-Centric Matching for 3D Multi-Object Tracking Performance Evaluation](https://mlsanity.com/paper/2506.04122/) (2025)

## Abstract (arXiv)

Object detection in autonomous driving consists in perceiving and locating instances of objects in multi-dimensional data, such as images or lidar scans. Very recently, multiple works are proposing to evaluate object detectors by measuring their ability to detect the objects that are most likely to interfere with the driving task. Detectors are then ranked according to their ability to detect the most relevant objects, rather than the highest number of objects. However there is little evidence so far that the relevance of predicted object may contribute to the safety and reliability improvement of the driving task. This position paper elaborates on a strategy, together with partial results, to i) configure and deploy object detectors that successfully extract knowledge on object relevance, and ii) use such knowledge to improve the trajectory planning task. We show that, given an object detector, filtering objects based on their relevance, in combination with the traditional confidence threshold, reduces the risk of missing relevant objects, decreases the likelihood of dangerous trajectories, and improves the quality of trajectories in general.