# Collision Risk Estimation via Loss Prediction in End-to-End Autonomous Driving

arXiv: https://arxiv.org/abs/2503.07425 · HTML: https://mlsanity.com/paper/2503.07425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 35 of its 49 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://mlsanity.com/paper/2207.07601/) (2022)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks](https://mlsanity.com/paper/1905.11001/) (2019)
- [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [GenAD: Generative End-to-End Autonomous Driving](https://mlsanity.com/paper/2402.11502/) (2024)
- [NeuRAD: Neural Rendering for Autonomous Driving](https://mlsanity.com/paper/2311.15260/) (2023)
- [Learning to drive from a world on rails](https://mlsanity.com/paper/2105.00636/) (2021)
- [Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals](https://mlsanity.com/paper/2106.15004/) (2021)
- [DEUP: Direct Epistemic Uncertainty Prediction](https://mlsanity.com/paper/2102.08501/) (2021)
- [Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks](https://mlsanity.com/paper/2107.07455/) (2021)
- [Differentiable Raycasting for Self-supervised Occupancy Forecasting](https://mlsanity.com/paper/2210.01917/) (2022)
- [NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving](https://mlsanity.com/paper/2404.07762/) (2024)
- [StretchBEV: Stretching Future Instance Prediction Spatially and Temporally](https://mlsanity.com/paper/2203.13641/) (2022)
- [PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving](https://mlsanity.com/paper/2311.08100/) (2023)
- [Probabilistic Prediction of Vehicle Semantic Intention and Motion](https://mlsanity.com/paper/1804.03629/) (2018)
- [Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling](https://mlsanity.com/paper/2301.01006/) (2023)
- [Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion](https://mlsanity.com/paper/2005.01935/) (2020)
- [URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates](https://mlsanity.com/paper/2307.03810/) (2023)

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

- [Auditing Latent-Space Monitors for Autonomous Driving](https://mlsanity.com/paper/2609.30557/) (2026)

## Abstract (arXiv)

Collision risk estimation and avoidance play central roles in the safety of autonomous driving (AD) systems. Recently emerged end-to-end AD systems gain collision avoidance ability by minimizing losses to penalize planning trajectories that are too close to other objects. Despite a significant collision rate during testing, most end-to-end planners do not explicitly quantify the collision risk in their outputs. To address this, we introduce RiskMonitor, an efficient plug-and-play module that interprets planning and motion tokens from state-of-the-art end-to-end planners to estimate collision risk. Inspired by loss prediction based uncertainty quantification, RiskMonitor predicts whether the collision loss -- commonly adopted to train end-to-end planners -- is positive along planned waypoints, framing collision risk estimation as a binary classification task. We evaluate RiskMonitor on the real-world nuScenes dataset (open-loop) and the neural-rendering based simulator, NeuroNCAP (closed-loop). Our token-driven method outperforms prediction-driven approaches, including deterministic rules, Gaussian mixture models, and Monte Carlo Dropout. When integrated with a simple braking policy, RiskMonitor improves collision avoidance ability by 66.5% in a closed-loop test on safety-critical scenarios. These results demonstrate that monitoring collision risk using plan and motion tokens enhances the safety of end-to-end AD without retraining it.