# Dynamically Conservative Self-Driving Planner for Long-Tail Cases

arXiv: https://arxiv.org/abs/2305.07497 · HTML: https://mlsanity.com/paper/2305.07497/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 17 of 17)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Improving Calibration for Long-Tailed Recognition](https://mlsanity.com/paper/2104.00466/) (2021)
- [Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning](https://mlsanity.com/paper/1803.00101/) (2018)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective](https://mlsanity.com/paper/2202.02215/) (2022)
- [FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation](https://mlsanity.com/paper/2102.12867/) (2021)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Waymo Public Road Safety Performance Data](https://mlsanity.com/paper/2011.00038/) (2020)
- [Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles](https://mlsanity.com/paper/2207.00788/) (2022)
- [Deep Predictive Models for Collision Risk Assessment in Autonomous Driving](https://mlsanity.com/paper/1711.10453/) (2017)
- [Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving](https://mlsanity.com/paper/1811.11277/) (2018)
- [Prediction-Based Reachability for Collision Avoidance in Autonomous Driving](https://mlsanity.com/paper/2011.12406/) (2020)

## Built on by (most cited first, 13 of 13)

- [Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.09800/) (2025)
- [Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies](https://mlsanity.com/paper/2401.12888/) (2024)
- [FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback](https://mlsanity.com/paper/2503.08162/) (2025)
- [Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network](https://mlsanity.com/paper/2502.05943/) (2025)
- [RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios](https://mlsanity.com/paper/2504.15541/) (2025)
- [SEAL: Vision-Language Model-Based Safe End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling](https://mlsanity.com/paper/2506.21041/) (2025)
- [RobKiNet: Robotic Kinematics Informed Neural Network for Optimal Robot Configuration Prediction](https://mlsanity.com/paper/2402.16281/) (2024)
- [Act Better by Timing: A timing-Aware Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2406.13223/) (2024)
- [Application of Multimodal Large Language Models in Autonomous Driving](https://mlsanity.com/paper/2412.16410/) (2024)
- [DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy](https://mlsanity.com/paper/2506.16720/) (2025)
- [LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving](https://mlsanity.com/paper/2512.24712/) (2025)
- [Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving](https://mlsanity.com/paper/2502.21134/) (2025)
- [Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions](https://mlsanity.com/paper/2505.12327/) (2025)

## Abstract (arXiv)

Self-driving vehicles (SDVs) are becoming reality but still suffer from "long-tail" challenges during natural driving: the SDVs will continually encounter rare, safety-critical cases that may not be included in the dataset they were trained. Some safety-assurance planners solve this problem by being conservative in all possible cases, which may significantly affect driving mobility. To this end, this work proposes a method to automatically adjust the conservative level according to each case's "long-tail" rate, named dynamically conservative planner (DCP). We first define the "long-tail" rate as an SDV's confidence to pass a driving case. The rate indicates the probability of safe-critical events and is estimated using the statistics bootstrapped method with historical data. Then, a reinforcement learning-based planner is designed to contain candidate policies with different conservative levels. The final policy is optimized based on the estimated "long-tail" rate. In this way, the DCP is designed to automatically adjust to be more conservative in low-confidence "long-tail" cases while keeping efficient otherwise. The DCP is evaluated in the CARLA simulator using driving cases with "long-tail" distributed training data. The results show that the DCP can accurately estimate the "long-tail" rate to identify potential risks. Based on the rate, the DCP automatically avoids potential collisions in "long-tail" cases using conservative decisions while not affecting the average velocity in other typical cases. Thus, the DCP is safer and more efficient than the baselines with fixed conservative levels, e.g., an always conservative planner. This work provides a technique to guarantee SDV's performance in unexpected driving cases without resorting to a global conservative setting, which contributes to solving the "long-tail" problem practically.