Think as Needed: Geometry-Driven Adaptive Perception for Autonomous Driving

• URL: http://arxiv.org/abs/2605.10117v1
• Date: Mon, 11 May 2026 07:34:13 GMT
• Title: Think as Needed: Geometry-Driven Adaptive Perception for Autonomous Driving
• Abstract summary: Current 3D detection models apply a fixed computation budget to every frame, wasting resources on simple scenes while lacking capacity for complex ones.<n>We propose Enhanced HOPE, an adaptive perception architecture that measures the geometric complexity of each incoming LiDAR frame.<n>On the nuScenes and CARLA benchmarks, Enhanced HOPE reduces latency by 38% on simple scenes with no accuracy loss, and improves mean Average Precision by 2.7 points on rare long-tail scenarios.
• Score: 4.320171553006484
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Autonomous driving scenes range from empty highways to dense intersections with dozens of interacting road users, yet current 3D detection models apply a fixed computation budget to every frame, wasting resources on simple scenes while lacking capacity for complex ones. Existing approaches compound this problem: Transformer-based interaction models scale quadratically with the number of detected objects, and frame-by-frame processing causes the system to immediately forget objects the moment they become occluded. We propose Enhanced HOPE, an adaptive perception architecture that measures the geometric complexity of each incoming LiDAR frame using an unsupervised statistical estimator and routes it through a shallow or deep processing path accordingly, requiring no manual scene labels. To keep interaction modeling efficient, we replace quadratic pairwise attention with a linear-time subspace-based network that groups nearby objects into clusters and processes them jointly. The computational savings from these two mechanisms free up resources for a persistent temporal memory module that retains previously detected objects and traffic rules across frames, enabling the system to recall occluded objects seconds after they disappear from view. On the nuScenes and CARLA benchmarks, Enhanced HOPE reduces latency by 38% on simple scenes with no accuracy loss, improves mean Average Precision by 2.7 points on rare long-tail scenarios, and tracks objects through occlusions lasting over 5 seconds, where all tested baselines fail.

Related papers

• REMIND: RE-Identification with Memory for INDoor Navigation [39.146761527401424]
  Online tracker designed for long-term multi-object re-identification of generic indoor objects from monocular RGB imagery.<n>Motivated by evidence from visual cognition that humans rely on accumulated appearance familiarity and spatial context rather than explicit self-localization.<n>On ScanNet++, it attains the highest IDF1 in every setting but one, end-to-end detection over all scenes.
  arXiv  Detail & Related papers  (2026-07-10T10:30:50Z)
• Lightweight Spatiotemporal Highway Lane Detection via 3D-ResNet and PINet with ROI-Aware Attention [0.5156484100374058]
  We propose two models that integrate a 3D-ResNet encoder with a Point Instance Network (PINet) decoder.<n>The architecture has been validated through offline training and real-time inference in the Autonomous Systems Laboratory at City, St George's University of London.
  arXiv  Detail & Related papers  (2026-04-02T15:47:16Z)
• oTTC: Object Time-to-Contact for Motion Estimation in Autonomous Driving [4.707950656037167]
  autonomous driving systems rely heavily on object detection to avoid collisions and drive safely. Monocular 3D object detectors try to solve this problem by directly predicting 3D bounding boxes and object velocities given a camera image. Recent research estimates time-to-contact in a per-pixel manner and suggests that it is more effective measure than velocity and depth combined. We propose per-object time-to-contact estimation by extending object detection models to additionally predict the time-to-contact attribute for each object.
  arXiv  Detail & Related papers  (2024-05-13T12:34:18Z)
• Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving [69.20604395205248]
  We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. We introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector. We conduct extensive experiments on the KITTI, runtime, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD.
  arXiv  Detail & Related papers  (2024-03-10T10:36:32Z)
• PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection [66.94819989912823]
  We propose a point-trajectory transformer with long short-term memory for efficient temporal 3D object detection. We use point clouds of current-frame objects and their historical trajectories as input to minimize the memory bank storage requirement. We conduct extensive experiments on the large-scale dataset to demonstrate that our approach performs well against state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-13T18:59:13Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• MonoPIC -- A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees [0.0]
  We propose an algorithm that classifies the intent of a single arbitrarily chosen pedestrian in a two dimensional frame into logic states. This bypasses the need to employ any relatively high latency deep-learning algorithms. The model was able to achieve an average testing accuracy of 83.56% with a reliable variance of 0.0042 while operating with an average latency of 48 milliseconds.
  arXiv  Detail & Related papers  (2023-04-01T02:42:24Z)
• Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes [82.4186966781934]
  We introduce a simple, efficient, and effective two-stage detector, termed as Ret3D. At the core of Ret3D is the utilization of novel intra-frame and inter-frame relation modules. With negligible extra overhead, Ret3D achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-18T03:48:58Z)
• Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding [61.57847727651068]
  Temporal sentence grounding aims to localize a target segment in an untrimmed video semantically according to a given sentence query. Most previous works focus on learning frame-level features of each whole frame in the entire video, and directly match them with the textual information. We propose a novel Motion- and Appearance-guided 3D Semantic Reasoning Network (MA3SRN), which incorporates optical-flow-guided motion-aware, detection-based appearance-aware, and 3D-aware object-level features.
  arXiv  Detail & Related papers  (2022-03-06T13:57:09Z)
• Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation [8.854112907350624]
  3D multi-object tracking plays a vital role in autonomous navigation. Many approaches detect objects in 2D RGB sequences for tracking, which is lack of reliability when localizing objects in 3D space. We propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames.
  arXiv  Detail & Related papers  (2020-11-25T16:14:40Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.