[Paper Review] Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving

Pranav Rajpurkar, Toki Migimatsu+5 more|arXiv (Cornell University)|Dec 7, 2015

Autonomous Vehicle Technology and Safety22 references4 citations

TL;DR

Driverseat introduces a crowdstrapping framework that leverages human intelligence to generate complex 3D lane labels and tag diverse driving scenarios for robust evaluation of autonomous driving systems. By combining crowd-sourced labeling via RoadEdit and scenario-based evaluation via TagEval, the system trains a convolutional neural network for lane detection and identifies performance weaknesses in challenging conditions like shadows, pavement changes, and off-ramps, significantly improving system robustness.

ABSTRACT

While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open field for scientific exploration. Broadly, there are two major developmental bottlenecks: the unavailability of comprehensively labeled datasets and of expressive evaluation strategies. Approaches for labeling datasets have relied on intensive hand-engineering, and strategies for evaluating learning systems have been unable to identify failure-case scenarios. Human intelligence offers an untapped approach for breaking through these bottlenecks. This paper introduces Driverseat, a technology for embedding crowds around learning systems for autonomous driving. Driverseat utilizes crowd contributions for (a) collecting complex 3D labels and (b) tagging diverse scenarios for ready evaluation of learning systems. We demonstrate how Driverseat can crowdstrap a convolutional neural network on the lane-detection task. More generally, crowdstrapping introduces a valuable paradigm for any technology that can benefit from leveraging the powerful combination of human and computer intelligence.

Motivation & Objective

• To address the bottleneck in autonomous driving research caused by the lack of comprehensively labeled datasets and robust evaluation strategies.
• To leverage human expertise in real-world driving scenarios to generate high-quality, complex 3D labels for training deep learning models.
• To develop a systematic way to evaluate learning systems across diverse, real-world driving conditions such as shadows, road curves, and pavement changes.
• To identify failure cases in autonomous driving perception systems through human-annotated scenario tagging, guiding targeted data collection and model improvement.
• To establish a paradigm of 'crowdstrapping'—integrating human and machine intelligence—to enhance the reliability and robustness of autonomous driving systems.

Proposed method

• Driverseat uses a web-based platform to collect 3D lane boundary annotations from crowd workers via the RoadEdit tool, enabling accurate labeling of complex road structures.
• The system projects 3D lane labels into 2D camera images to create a large-scale, high-quality dataset for training convolutional neural networks on lane detection.
• A convolutional neural network is trained on the crowd-annotated dataset and evaluated using precision and recall metrics across varying depths and scenarios.
• TagEval framework enables annotators to tag specific road conditions—such as shadows, off-ramps, and pavement changes—allowing for scenario-specific performance evaluation.
• The system uses human-annotated scenario tags to isolate and analyze model failures, enabling targeted data collection and model refinement.
• Initial ground-truth estimates are generated automatically to reduce human workload, improving efficiency in the annotation pipeline.

Experimental results

Research questions

• RQ1 How can human intelligence be effectively integrated into the training and evaluation pipeline of autonomous driving perception systems?
• RQ2 What types of real-world driving scenarios most challenge deep learning models for lane detection?
• RQ3 Can crowd-sourced labeling produce high-quality, complex 3D labels that outperform automated or synthetic labeling methods?
• RQ4 To what extent can scenario-based human tagging improve the identification of model failure modes?
• RQ5 How can human and machine intelligence be systematically combined to enhance the robustness of autonomous driving systems?

Key findings

• The convolutional neural network achieved strong performance on ego-lane boundary detection under general conditions, with precision and recall metrics showing high accuracy at short to medium distances.
• Performance declined significantly in the presence of shadows and pavement changes, which caused the model to misinterpret reflective patches as lane markings.
• Off-ramps posed a major challenge, particularly for detecting the splitting lane, with the model frequently missing emerging lanes.
• The network remained robust to road curves and occlusions, correctly detecting lane boundaries even when vehicles partially obscured the view.
• TagEval revealed that shadows and pavement changes were the most difficult scenarios, with performance dropping more than 20% in some cases compared to standard conditions.
• The system successfully identified specific failure patterns, enabling targeted data collection to improve model robustness in high-risk scenarios.