# USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving

arXiv: https://arxiv.org/abs/2209.10368 · HTML: https://mlsanity.com/paper/2209.10368/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in VLMs for autonomous driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Placed in

- End-to-end autonomous driving / [VLMs for autonomous driving](https://mlsanity.com/driving/vlms-for-autonomous-driving/)

## Builds on (most cited first, 13 of 13)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://mlsanity.com/paper/2104.10956/) (2021)
- [Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://mlsanity.com/paper/2107.14160/) (2021)
- [SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds](https://mlsanity.com/paper/2004.02774/) (2020)
- [Deep Fitting Degree Scoring Network for Monocular 3D Object Detection](https://mlsanity.com/paper/1904.12681/) (2019)
- [Learning to Evaluate Perception Models Using Planner-Centric Metrics](https://mlsanity.com/paper/2004.08745/) (2020)
- [Revisiting 3D Object Detection From an Egocentric Perspective](https://mlsanity.com/paper/2112.07787/) (2021)
- [Safety-Aware Hardening of 3D Object Detection Neural Network Systems](https://mlsanity.com/paper/2003.11242/) (2020)
- [Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection](https://mlsanity.com/paper/2202.05123/) (2022)

## Built on by (most cited first, 5 of 5)

- [Simulation-based Safety Assurance for an AVP System incorporating Learning-Enabled Components](https://mlsanity.com/paper/2311.03362/) (2023)
- [A Safety-Adapted Loss for Pedestrian Detection in Automated Driving](https://mlsanity.com/paper/2402.02986/) (2024)
- [FuzzRisk: Online Collision Risk Estimation for Autonomous Vehicles based on Depth-Aware Object Detection via Fuzzy Inference](https://mlsanity.com/paper/2411.08060/) (2024)
- [Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving](https://mlsanity.com/paper/2603.28029/) (2026)
- [Safety-Aligned 3D Object Detection: Single-Vehicle, Cooperative, and End-to-End Perspectives](https://mlsanity.com/paper/2604.03325/) (2026)

## Abstract (arXiv)

In this work, we consider the safety-oriented performance of 3D object detectors in autonomous driving contexts. Specifically, despite impressive results shown by the mass literature, developers often find it hard to ensure the safe deployment of these learning-based perception models. Attributing the challenge to the lack of safety-oriented metrics, we hereby present uncompromising spatial constraints (USC), which characterize a simple yet important localization requirement demanding the predictions to fully cover the objects when seen from the autonomous vehicle. The constraints, as we formulate using the perspective and bird's-eye views, can be naturally reflected by quantitative measures, such that having an object detector with a higher score implies a lower risk of collision. Finally, beyond model evaluation, we incorporate the quantitative measures into common loss functions to enable safety-oriented fine-tuning for existing models. With experiments using the nuScenes dataset and a closed-loop simulation, our work demonstrates such considerations of safety notions at the perception level not only improve model performances beyond accuracy but also allow for a more direct linkage to actual system safety.