# Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios

arXiv: https://arxiv.org/abs/2310.17381 · HTML: https://mlsanity.com/paper/2310.17381/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 19 of its 59 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 19 of 19)

- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Optimization-Based Collision Avoidance](https://mlsanity.com/paper/1711.03449/) (2017)
- [Interactive multi-modal motion planning with Branch Model Predictive Control](https://mlsanity.com/paper/2109.05128/) (2021)
- [Robust Model Predictive Path Integral Control: Analysis and Performance Guarantees](https://mlsanity.com/paper/2102.09027/) (2021)
- [Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version](https://mlsanity.com/paper/2009.09381/) (2020)
- [A Two-Stage Optimization-based Motion Planner for Safe Urban Driving](https://mlsanity.com/paper/2002.02215/) (2020)
- [Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios](https://mlsanity.com/paper/2112.07624/) (2021)
- [Uncertainty-aware Contact-safe Model-based Reinforcement Learning](https://mlsanity.com/paper/2010.08169/) (2020)
- [Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach](https://mlsanity.com/paper/2203.14913/) (2022)
- [A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving](https://mlsanity.com/paper/1902.09068/) (2019)
- [Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty](https://mlsanity.com/paper/2106.02801/) (2021)
- [Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach](https://mlsanity.com/paper/2206.13319/) (2022)
- [A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles](https://mlsanity.com/paper/1802.09356/) (2018)
- [Safe Trajectory Tracking in Uncertain Environments](https://mlsanity.com/paper/2001.11602/) (2020)
- [Enhancing Lattice-based Motion Planning with Introspective Learning and Reasoning](https://mlsanity.com/paper/2005.07385/) (2020)
- [Real-time motion planning and decision-making for a group of differential drive robots under connectivity constraints using robust MPC and mixed-integer programming](https://mlsanity.com/paper/2206.00097/) (2022)
- [Stochastic Artificial Potentials for Online Safe Navigation](https://mlsanity.com/paper/1701.00033/) (2016)
- [Efficient MPC for Emergency Evasive Maneuvers, Part I: Hybridization of the Nonlinear Problem](https://mlsanity.com/paper/2310.00715/) (2023)
- [Efficient MPC for Emergency Evasive Maneuvers, Part II: Comparative Assessment for Hybrid Control](https://mlsanity.com/paper/2310.00716/) (2023)

## Built on by (most cited first, 3 of 3)

- [Risk-Aware Vehicle Trajectory Prediction Under Safety-Critical Scenarios](https://mlsanity.com/paper/2407.13480/) (2024)
- [MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving](https://mlsanity.com/paper/2512.03795/) (2025)
- [Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers](https://mlsanity.com/paper/2509.07464/) (2025)

## Abstract (arXiv)

Uncertainty in the behavior of other traffic participants is a crucial factor in collision avoidance for automated driving; here, stochastic metrics could avoid overly conservative decisions. This paper introduces a Stochastic Model Predictive Control (SMPC) planner for emergency collision avoidance in highway scenarios to proactively minimize collision risk while ensuring safety through chance constraints. To guarantee that the emergency trajectory can be attained, we incorporate nonlinear tire dynamics in the prediction model of the ego vehicle. Further, we exploit Max-Min-Plus-Scaling (MMPS) approximations of the nonlinearities to avoid conservatism, enforce proactive collision avoidance, and improve computational efficiency in terms of performance and speed. Consequently, our contributions include integrating a dynamic ego vehicle model into the SMPC planner, introducing the MMPS approximation for real-time implementation in emergency scenarios, and integrating SMPC with hybridized chance constraints and risk minimization. We evaluate our SMPC formulation in terms of proactivity and efficiency in various hazardous scenarios. Moreover, we demonstrate the effectiveness of our proposed approach by comparing it with a state-of-the-art SMPC planner and we validate that the generated trajectories can be attained using a high-fidelity vehicle model in IPG CarMaker.