# Integrity Monitoring of 3D Object Detection in Automated Driving Systems using Raw Activation Patterns and Spatial Filtering

arXiv: https://arxiv.org/abs/2405.07600 · HTML: https://mlsanity.com/paper/2405.07600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 14 of 14)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020)
- [Dropout Sampling for Robust Object Detection in Open-Set Conditions](https://mlsanity.com/paper/1710.06677/) (2017)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection](https://mlsanity.com/paper/1804.05132/) (2018)
- [Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving](https://mlsanity.com/paper/1909.12358/) (2019)
- [Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars](https://mlsanity.com/paper/1707.00051/) (2017)
- [Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends](https://mlsanity.com/paper/2101.01364/) (2021)
- [Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations](https://mlsanity.com/paper/2403.01172/) (2024)
- [Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation Patterns](https://mlsanity.com/paper/2404.07685/) (2024)

## Abstract (arXiv)

The deep neural network (DNN) models are widely used for object detection in automated driving systems (ADS). Yet, such models are prone to errors which can have serious safety implications. Introspection and self-assessment models that aim to detect such errors are therefore of paramount importance for the safe deployment of ADS. Current research on this topic has focused on techniques to monitor the integrity of the perception mechanism in ADS. Existing introspection models in the literature, however, largely concentrate on detecting perception errors by assigning equal importance to all parts of the input data frame to the perception module. This generic approach overlooks the varying safety significance of different objects within a scene, which obscures the recognition of safety-critical errors, posing challenges in assessing the reliability of perception in specific, crucial instances. Motivated by this shortcoming of state of the art, this paper proposes a novel method integrating raw activation patterns of the underlying DNNs, employed by the perception module, analysis with spatial filtering techniques. This novel approach enhances the accuracy of runtime introspection of the DNN-based 3D object detections by selectively focusing on an area of interest in the data, thereby contributing to the safety and efficacy of ADS perception self-assessment processes.