# Safe Planning for Self-Driving Via Adaptive Constrained ILQR

arXiv: https://arxiv.org/abs/2003.02757 · HTML: https://mlsanity.com/paper/2003.02757/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2207.04361/) (2022)
- [Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles](https://mlsanity.com/paper/2209.07042/) (2022)
- [IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning](https://mlsanity.com/paper/2311.02850/) (2023)
- [RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment](https://mlsanity.com/paper/2406.04451/) (2024)
- [Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles](https://mlsanity.com/paper/2309.14566/) (2023)
- [PlanScope: Learning to Plan Within Decision Scope for Urban Autonomous Driving](https://mlsanity.com/paper/2411.00476/) (2024)
- [Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR](https://mlsanity.com/paper/2503.17398/) (2025)
- [Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions](https://mlsanity.com/paper/2505.12327/) (2025)

## Abstract (arXiv)

Constrained Iterative Linear Quadratic Regulator (CILQR), a variant of ILQR, has been recently proposed for motion planning problems of autonomous vehicles to deal with constraints such as obstacle avoidance and reference tracking. However, the previous work considers either deterministic trajectories or persistent prediction for target dynamical obstacles. The other drawback is lack of generality - it requires manual weight tuning for different scenarios. In this paper, two significant improvements are achieved. Firstly, a two-stage uncertainty-aware prediction is proposed. The short-term prediction with safety guarantee based on reachability analysis is responsible for dealing with extreme maneuvers conducted by target vehicles. The long-term prediction leveraging an adaptive least square filter preserves the long-term optimality of the planned trajectory since using reachability only for long-term prediction is too pessimistic and makes the planner over-conservative. Secondly, to allow a wider coverage over different scenarios and to avoid tedious parameter tuning case by case, this paper designs a scenario-based analytical function taking the states from the ego vehicle and the target vehicle as input, and carrying weights of a cost function as output. It allows the ego vehicle to execute multiple behaviors (such as lane-keeping and overtaking) under a single planner. We demonstrate safety, effectiveness, and real-time performance of the proposed planner in simulations.