# Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety

arXiv: https://arxiv.org/abs/1903.00640 · HTML: https://mlsanity.com/paper/1903.00640/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 2 Mar 2019.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving](https://mlsanity.com/paper/1808.05819/) (2018)
- [Conditional Affordance Learning for Driving in Urban Environments](https://mlsanity.com/paper/1806.06498/) (2018)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)
- [Deep Object-Centric Policies for Autonomous Driving](https://mlsanity.com/paper/1811.05432/) (2018)
- [A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning](https://mlsanity.com/paper/1707.02515/) (2017)

## Built on by (most cited first, 30 of 49)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets](https://mlsanity.com/paper/2404.19264/) (2024)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving](https://mlsanity.com/paper/2208.12263/) (2022)
- [Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models](https://mlsanity.com/paper/2104.10558/) (2021)
- [A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning](https://mlsanity.com/paper/2101.06778/) (2021)
- [Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks](https://mlsanity.com/paper/2406.15149/) (2024)
- [DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks](https://mlsanity.com/paper/2108.05030/) (2021)
- [PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving](https://mlsanity.com/paper/2011.00509/) (2020)
- [Explainable Hierarchical Imitation Learning for Robotic Drink Pouring](https://mlsanity.com/paper/2105.07348/) (2021)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [VIEW: Visual Imitation Learning with Waypoints](https://mlsanity.com/paper/2404.17906/) (2024)
- [End-to-end Autonomous Driving using Deep Learning: A Systematic Review](https://mlsanity.com/paper/2311.18636/) (2023)
- [Parameterized Decision-making with Multi-modal Perception for Autonomous Driving](https://mlsanity.com/paper/2312.11935/) (2023)
- [Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/2401.11792/) (2024)
- [Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving](https://mlsanity.com/paper/2302.03939/) (2023)
- [Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations](https://mlsanity.com/paper/2305.19904/) (2023)
- [Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving](https://mlsanity.com/paper/2110.04052/) (2021)
- [Tackling Real-World Autonomous Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.02162/) (2022)
- [RAIL: Reachability-Aided Imitation Learning for Safe Policy Execution](https://mlsanity.com/paper/2409.19190/) (2024)
- [Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.12717/) (2023)
- [Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning](https://mlsanity.com/paper/2303.13986/) (2023)
- [Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving](https://mlsanity.com/paper/2304.08280/) (2023)
- [IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors](https://mlsanity.com/paper/2306.15228/) (2023)
- [Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control](https://mlsanity.com/paper/2311.18393/) (2023)
- [Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2403.18209/) (2024)
- [Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning](https://mlsanity.com/paper/2202.10002/) (2022)
- [NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception](https://mlsanity.com/paper/2307.06615/) (2023)
- [Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning](https://mlsanity.com/paper/2507.12977/) (2025)
- [Learning robust driving policies without online exploration](https://mlsanity.com/paper/2103.08070/) (2021)

## Abstract (arXiv)

The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.