# SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2503.22541 · HTML: https://mlsanity.com/paper/2503.22541/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 54 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 28 of 28)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Multiple Futures Prediction](https://mlsanity.com/paper/1911.00997/) (2019)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://mlsanity.com/paper/1811.02146/) (2018)
- [Universal Prompt Tuning for Graph Neural Networks](https://mlsanity.com/paper/2209.15240/) (2022)
- [TPNet: Trajectory Proposal Network for Motion Prediction](https://mlsanity.com/paper/2004.12255/) (2020)
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://mlsanity.com/paper/1805.05499/) (2018)
- [GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/1907.07792/) (2019)
- [Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving](https://mlsanity.com/paper/2109.06446/) (2021)
- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models](https://mlsanity.com/paper/2312.03543/) (2023)
- [BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2312.06371/) (2023)
- [GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model](https://mlsanity.com/paper/2209.07857/) (2022)
- [CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting](https://mlsanity.com/paper/2503.07234/) (2025)
- [S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2206.10902/) (2022)
- [Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance](https://mlsanity.com/paper/2103.14231/) (2021)
- [A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving](https://mlsanity.com/paper/2402.19251/) (2024)
- [CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions](https://mlsanity.com/paper/2407.17757/) (2024)
- [Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments](https://mlsanity.com/paper/2402.04318/) (2024)
- [MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2405.01266/) (2024)
- [NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving](https://mlsanity.com/paper/2412.11682/) (2024)
- [A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environment](https://mlsanity.com/paper/2404.17520/) (2024)
- [Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights](https://mlsanity.com/paper/2502.20084/) (2025)
- [Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2405.02145/) (2024)
- [DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving](https://mlsanity.com/paper/2409.10330/) (2024)
- [DEMO: A Dynamics-Enhanced Learning Model for Multi-Horizon Trajectory Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2412.20784/) (2024)

## Built on by (most cited first, 2 of 2)

- [Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction](https://mlsanity.com/paper/2505.06856/) (2025)
- [REACT: Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving](https://mlsanity.com/paper/2505.11474/) (2025)

## Abstract (arXiv)

Accurate motion forecasting is essential for the safety and reliability of autonomous driving (AD) systems. While existing methods have made significant progress, they often overlook explicit safety constraints and struggle to capture the complex interactions among traffic agents, environmental factors, and motion dynamics. To address these challenges, we present SafeCast, a risk-responsive motion forecasting model that integrates safety-aware decision-making with uncertainty-aware adaptability. SafeCast is the first to incorporate the Responsibility-Sensitive Safety (RSS) framework into motion forecasting, encoding interpretable safety rules--such as safe distances and collision avoidance--based on traffic norms and physical principles. To further enhance robustness, we introduce the Graph Uncertainty Feature (GUF), a graph-based module that injects learnable noise into Graph Attention Networks, capturing real-world uncertainties and enhancing generalization across diverse scenarios. We evaluate SafeCast on four real-world benchmark datasets--Next Generation Simulation (NGSIM), Highway Drone (HighD), ApolloScape, and the Macao Connected Autonomous Driving (MoCAD)--covering highway, urban, and mixed-autonomy traffic environments. Our model achieves state-of-the-art (SOTA) accuracy while maintaining a lightweight architecture and low inference latency, underscoring its potential for real-time deployment in safety-critical AD systems.