# Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy

arXiv: https://arxiv.org/abs/1610.01238 · HTML: https://mlsanity.com/paper/1610.01238/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2016.

## Built on by (most cited first, 28 of 28)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
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- [From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence](https://mlsanity.com/paper/2110.15245/) (2021)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1909.06953/) (2019)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory](https://mlsanity.com/paper/2010.10393/) (2020)
- [Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images](https://mlsanity.com/paper/2109.07245/) (2021)
- [Energy-based Legged Robots Terrain Traversability Modeling via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2207.03034/) (2022)
- [System-level Safety Guard: Safe Tracking Control through Uncertain Neural Network Dynamics Models](https://mlsanity.com/paper/2312.06810/) (2023)
- [Learning Semantic Traversability with Egocentric Video and Automated Annotation Strategy](https://mlsanity.com/paper/2406.02989/) (2024)
- [Hierarchical Road Topology Learning for Urban Map-less Driving](https://mlsanity.com/paper/2104.00084/) (2021)
- [AutoGraph: Predicting Lane Graphs from Traffic Observations](https://mlsanity.com/paper/2306.15410/) (2023)
- [TrackletMapper: Ground Surface Segmentation and Mapping from Traffic Participant Trajectories](https://mlsanity.com/paper/2209.05247/) (2022)
- [Learning to Predict Navigational Patterns from Partial Observations](https://mlsanity.com/paper/2304.13242/) (2023)
- [Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method](https://mlsanity.com/paper/2402.05747/) (2024)
- [Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision](https://mlsanity.com/paper/2107.01784/) (2021)
- [Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Maps](https://mlsanity.com/paper/2603.27553/) (2026)
- [Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network](https://mlsanity.com/paper/2310.20148/) (2023)
- [CogSense: A Cognitively Inspired Framework for Perception Adaptation](https://mlsanity.com/paper/2107.10456/) (2021)
- [Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task](https://mlsanity.com/paper/2212.09438/) (2022)
- [YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless Automated Diving](https://mlsanity.com/paper/2402.00989/) (2024)
- [Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles](https://mlsanity.com/paper/2504.01336/) (2025)
- [Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP](https://mlsanity.com/paper/2509.11270/) (2025)
- [GHOST: Ground-projected Hypotheses from Observed Structure-from-Motion Trajectories](https://mlsanity.com/paper/2603.20583/) (2026)
- [Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion](https://mlsanity.com/paper/2610.13872/) (2026)

## Abstract (arXiv)

We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.