# Safety-driven Interactive Planning for Neural Network-based Lane Changing

arXiv: https://arxiv.org/abs/2201.09112 · HTML: https://mlsanity.com/paper/2201.09112/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2022.

## Built on by (most cited first, 6 of 6)

- [Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments](https://mlsanity.com/paper/2209.15090/) (2022)
- [Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction](https://mlsanity.com/paper/2205.14230/) (2022)
- [POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems](https://mlsanity.com/paper/2304.01218/) (2023)
- [Joint Differentiable Optimization and Verification for Certified Reinforcement Learning](https://mlsanity.com/paper/2201.12243/) (2022)
- [Learning Representation for Anomaly Detection of Vehicle Trajectories](https://mlsanity.com/paper/2303.05000/) (2023)
- [Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling](https://mlsanity.com/paper/2309.09317/) (2023)

## Abstract (arXiv)

Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the safety of systems with neural network based components, especially in dense and highly interactive traffic environments. In this work, we propose a safety-driven interactive planning framework for neural network-based lane changing. To prevent over conservative planning, we identify the driving behavior of surrounding vehicles and assess their aggressiveness, and then adapt the planned trajectory for the ego vehicle accordingly in an interactive manner. The ego vehicle can proceed to change lanes if a safe evasion trajectory exists even in the predicted worst case; otherwise, it can stay around the current lateral position or return back to the original lane. We quantitatively demonstrate the effectiveness of our planner design and its advantage over baseline methods through extensive simulations with diverse and comprehensive experimental settings, as well as in real-world scenarios collected by an autonomous vehicle company.