Single-Eye View: Monocular Real-time Perception Package for Autonomous Driving
This paper presents LRHPerception, a unified monocular perception package that addresses the computational burden of multi-camera autonomous driving pipelines by integrating object tracking, trajectory prediction, road segmentation, and depth estimation into a single real-time system processing at 29 FPS on one GPU. The core innovation lies in sharing a Swin Transformer backbone across modules while introducing task-specific optimizations like C-BYTE tracking with camera-motion compensation and a coarse-to-fine depth estimator. This matters because it offers an interpretable middle ground between black-box end-to-end driving and expensive bird's-eye-view mapping systems.
The paper delivers a pragmatic engineering contribution that successfully integrates multiple perception tasks with genuine speed improvements through shared feature extraction. The modular architecture demonstrates efficient information sharing, with the integration technique alone providing a 2× speedup over serial processing by leveraging shared backbones and skip connections.
The C-BYTE tracking module's incorporation of Lucas-Kanade optical flow and RANSAC-based affine transformation to compensate for camera ego-motion represents a concrete refinement to BYTE-style trackers, yielding measurable improvements on MOT17 (76.9% MOTA vs 76.6% for ByteTrack). The coarse-refine depth estimation design achieves competitive KITTI metrics (RMS 0.229) while operating at 42 FPS, demonstrating that simplified C2f-based decoder architectures can maintain accuracy comparable to heavier alternatives.
The headline claim of "555% acceleration over the fastest mapping technique" conflates fundamentally different input modalities—monocular versus multi-camera fusion—comparing LRHPerception against methods like Uni-AD and BEVerse that process surround-view inputs (Table VI). The "SOTA in series" baseline of 1.8 FPS lacks clarity regarding which specific models are chained and whether comparable backbones are used. Furthermore, the trajectory prediction module is trained exclusively on pedestrian datasets (JAAD/PIE) while other modules use KITTI and Cityscapes, raising questions about unified performance in scenarios requiring simultaneous vehicle and road understanding.
The paper provides no end-to-end driving metrics or safety-critical performance analysis that would validate the system's utility for actual autonomous navigation, focusing instead on modular benchmarks that may not transfer to integrated real-world behavior.
While individual modules show improvements over task-specific baselines (tracking vs ByteTrack on MOT17, depth vs VA-DepthNet on KITTI), the cross-dataset training approach—using different datasets for different modules—precludes demonstrating that the integrated system actually works cohesively on a single consistent test set. The comparison to multi-camera mapping methods in Table VI is problematic because inputs are incomparable; monocular systems inherently receive less information than surround-view setups, making the speedup figures misleading without accounting for the radical difference in input complexity and environmental coverage.
The authors provide a GitHub repository link and specify the Swin Transformer backbone and RTX 3090 GPU for testing, but critical implementation details appear empirically tuned without systematic ablation. The optical flow hyperparameters ($\theta_{th}=0.9$, $a=210$) are stated as "empirically determined" without methodological justification, and the CVAE pre-training details for the trajectory predictor remain unspecified. The loss weighting ($\lambda_{seg}=5$, others $=1$) is similarly empirical, potentially blocking exact reproduction without extensive hyperparameter search.
Amidst the rapid advancement of camera-based autonomous driving technology, effectiveness is often prioritized with limited attention to computational efficiency. To address this issue, this paper introduces LRHPerception, a real-time monocular perception package for autonomous driving that uses single-view camera video to interpret the surrounding environment. The proposed system combines the computational efficiency of end-to-end learning with the rich representational detail of local mapping methodologies. With significant improvements in object tracking and prediction, road segmentation, and depth estimation integrated into a unified framework, LRHPerception processes monocular image data into a five-channel tensor consisting of RGB, road segmentation, and pixel-level depth estimation, augmented with object detection and trajectory prediction. Experimental results demonstrate strong performance, achieving real-time processing at 29 FPS on a single GPU, representing a 555% speedup over the fastest mapping-based approach.
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