# Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks

arXiv: https://arxiv.org/abs/2106.13052 · HTML: https://mlsanity.com/paper/2106.13052/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 83 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 21 of 21)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020)
- [Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning](https://mlsanity.com/paper/2001.08726/) (2020)
- [The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes](https://mlsanity.com/paper/1903.01568/) (2019)
- [Conditional Generative Neural System for Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1905.01631/) (2019)
- [The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale](https://mlsanity.com/paper/1909.04422/) (2019)
- [SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic](https://mlsanity.com/paper/1911.04074/) (2019)
- [Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships](https://mlsanity.com/paper/2011.04251/) (2020)
- [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)
- [Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections](https://mlsanity.com/paper/1904.05423/) (2019)
- [Cooperative Driving at Unsignalized Intersections Using Tree Search](https://mlsanity.com/paper/1902.01024/) (2019)
- [Cooperative Decision Making of Connected Automated Vehicles at Multi-lane Merging Zone: A Coalitional Game Approach](https://mlsanity.com/paper/2103.07887/) (2021)
- [Distributed Cooperative Driving in Multi-Intersection Road Networks](https://mlsanity.com/paper/2104.10313/) (2021)
- [Decentralized Optimal Control for Connected Automated Vehicles at Intersections Including Left and Right Turns](https://mlsanity.com/paper/1703.06956/) (2017)
- [Cooperative Schedule-Driven Intersection Control with Connected and Autonomous Vehicles](https://mlsanity.com/paper/1907.01984/) (2019)
- [Priority-based coordination of mobile robots](https://mlsanity.com/paper/1410.0879/) (2014)

## Built on by (most cited first, 15 of 15)

- [SIND: A Drone Dataset at Signalized Intersection in China](https://mlsanity.com/paper/2209.02297/) (2022)
- [UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving](https://mlsanity.com/paper/2503.24381/) (2025)
- [Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving](https://mlsanity.com/paper/2311.11056/) (2023)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2403.13674/) (2024)
- [Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2305.07487/) (2023)
- [Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm](https://mlsanity.com/paper/2503.03796/) (2025)
- [Robust Driving Policy Learning with Guided Meta Reinforcement Learning](https://mlsanity.com/paper/2307.10160/) (2023)
- [Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections](https://mlsanity.com/paper/2202.10124/) (2022)
- [Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions](https://mlsanity.com/paper/2310.20561/) (2023)
- [Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments](https://mlsanity.com/paper/2407.15839/) (2024)
- [Multi-Agent Probabilistic Ensembles with Trajectory Sampling for Connected Autonomous Vehicles](https://mlsanity.com/paper/2312.13910/) (2023)
- [Online Sequence Clustering Algorithm for Video Trajectory Analysis](https://mlsanity.com/paper/2305.08418/) (2023)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks](https://mlsanity.com/paper/2409.00622/) (2024)

## Abstract (arXiv)

Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.