3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances primarily rely on structural 3D surrogates (e.g., bird’s eye view) built from front-view image features and camera parameters. However, the depth ambiguity in monocular images inevitably causes misalignment between the constructed surrogate feature map and the original image, posing a great challenge for accurate lane detection. To address the above issue, we present a novel LATR model, an end-to-end 3D lane detector that uses 3D-aware front-view features without transformed view representation. Specifically, LATR detects 3D lanes via cross-attention based on query and key-value pairs, constructed using our lane-aware query generator and dynamic 3D ground positional embedding. On the one hand, each query is generated based on 2D lane-aware features and adopts a hybrid embedding to enhance the lane information. On the other hand, 3D space information is injected as positional embedding from an iteratively-updated 3D ground plane. LATR outperforms previous state-of-the-art methods on both synthetic Apollo and realistic OpenLane and ONCE-3DLanes by large margins (e.g., 11.4 gain in terms of F1 score on OpenLane). Code will be released at https://github.com/JMoonr/LATR.

3D lane detection based on LiDAR point clouds is a challenging task that requires precise locations, accurate topologies, and distinguishable instances. In this paper, we propose a dual-level shape attention network (DSANet) with two branches for high-precision 3D lane predictions. Specifically, one branch predicts the refined lane segment shapes and the shape embeddings that encode the approximate lane instance shapes, the other branch detects the coarse-grained structures of the lane instances. In the training stage, two-level shape matching loss functions are introduced to jointly optimize the shape parameters of the twobranch outputs, which are simple yet effective for precision enhancement. Furthermore, a shape-guided segments aggregator is proposed to help local lane segments aggregate into complete lane instances, according to the differences of instance shapes predicted at different levels. Experiments conducted on our BEV-3DLanes dataset demonstrate that our method outperforms previous methods.

This paper introduces a new memory scheduling policy called LAMS, which is inspired by a recently proposed memory architecture and targets for future high capacity memory systems. As memory capacity increases, the bit-lines connected to memory row buffers become much longer, dramatically lengthening memory access latency, due to increased parasitic capacitance. Recent study has proposed to partition long bit-lines into near and far (relative to the row buffer) segments via inserting isolation transistors such that access to near segment can be accomplished much faster, while access to far segment remains nearly the same. However, how to effectively leverage the new memory architecture still remains unexplored. We suggest to take advantage of this new memory architecture via performing latency-aware memory scheduling for pending requests to explore their performance potentials. In this scheduling policy, each memory request is classified to one of the following three categories, row-buffer hit, near-buffer, and far-buffer. Based on the classification, it issues requests in the order of row-buffer hit → near-buffer → far-buffer. In doing so, it avoids long-latency requests blocking short-latency memory requests, reducing total memory queuing time in the memory controller and improving overall memory performance. Our evaluation results on a simulated memory system show that comparing with the commonly used FR-FCFS scheduler, our LAMS improves performance and energy efficiency by up to 20.6% and 34%, respectively. Even comparing with the four competitive schedulers chosen from memory scheduling champion (MSC), LAMS still improves performance and energy efficiency by up to 6.1% and 23.4%, respectively.