Fast Scene Understanding for Autonomous Driving (1708.02550v1)

Abstract: Most approaches for instance-aware semantic labeling traditionally focus on accuracy. Other aspects like runtime and memory footprint are arguably as important for real-time applications such as autonomous driving. Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture, in this paper we present a real-time efficient implementation based on ENet that solves three autonomous driving related tasks at once: semantic scene segmentation, instance segmentation and monocular depth estimation. Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks. The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately.

• The paper demonstrates a unified multitask ENet architecture that integrates semantic segmentation, instance segmentation, and depth estimation for rapid scene understanding.
• It achieves real-time processing at 21 fps on Cityscapes while delivering competitive semantic segmentation accuracy with 59.3% IoU.
• The design uses a discriminative loss for instance segmentation and a reverse Huber loss for robust depth estimation, balancing speed and precision in autonomous driving.

An Efficient Multitask Architecture for Real-Time Scene Understanding in Autonomous Driving

The paper "Fast Scene Understanding for Autonomous Driving" addresses the crucial challenge of efficiently executing multiple computer vision tasks concurrently in the context of autonomous vehicles. The authors, Neven et al., propose an innovative integration of semantic segmentation, instance segmentation, and monocular depth estimation within a single branched architecture based on ENet. The objective of the research is to achieve real-time processing rates while maintaining competitive accuracy, making it suitable for practical applications in autonomous driving where computational resources are limited.

Core Contributions

1. Multitask Learning in a Unified Architecture: The authors present a multitask approach via a branched ENet model with a shared encoder and distinct decoders for individual tasks. This design leverages shared computations and promotes efficiency, achieving a processing speed of 21 frames per second at a 1024x512 resolution on the Cityscapes dataset—demonstrating feasibility for real-time processing.
2. Instance Segmentation without Detection: The paper adopts a discriminative loss function to enable instance segmentation without requiring a detect-and-segment framework. This allows for semantic and instance understanding in a more efficient feed-forward manner, particularly beneficial for applications demanding rapid inference.
3. Enhanced Depth Prediction: Incorporating the reverse Huber loss, known for its balance between handling outliers and preserving small details, the depth estimation module demonstrates robust performance in varied distance ranges. The proposed method effectively predicts depth by adapting to the nuances of urban driving scenes.
4. Evaluation across Metrics: The quantitative results demonstrate comparable semantic segmentation with 59.3% IoU using a combined network compared to the standalone ENet. Additionally, the multitask network effectively balances memory consumption, enhancing speed without a detrimental impact on accuracy.

Implications and Future Directions

The proposed framework, while not surpassing state-of-the-art benchmarks on accuracy, significantly reduces computational demands and improves processing speed, highlighting the trade-off between speed and accuracy. This positions the method as a viable baseline for applications that necessitate rapid response times, such as autonomous driving, where latency can impact operational safety.

Moving forward, the paper opens pathways for exploring the optimization of multitask learning frameworks, particularly examining dynamic weight allocation among tasks to optimize performance metrics further. Additionally, future research may investigate integrating more complex environmental understanding tasks, including motion prediction and event detection, within similar architectures.

The work contributes to the broader discourse on efficient neural network architectures for systems requiring real-time responsiveness, positing that intelligently designed shared encoders can augment the synergy between related tasks, yielding faster, low-memory solutions conducive to embedded systems used in the automotive industry.

In summary, the paper advances the capability of vision systems in autonomous cars by prioritizing rapid execution alongside competitive task performance, addressing a pivotal challenge in applied artificial intelligence and opening the floor for subsequent innovations in efficient multitask networks.