Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories

• URL: http://arxiv.org/abs/2301.09194v1
• Date: Sun, 22 Jan 2023 20:16:12 GMT
• Title: Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories
• Authors: Hanlin Chen, Renyuan Luo, Yiheng Feng
• Abstract summary: This paper presents a prototype that introduces crowdsourcing trajectories information into the mapping process to enhance CAV's understanding on the drivable area and traffic rules. The proposed method is compared with SLAM without any human driving information. Our method has adapted well with the downstream path planning and vehicle control module, and the CAV did not violate driving rule, which a pure SLAM method did not achieve.
• Score: 7.5390770178143045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping include high definition map (HD map) or real-time Simultaneous Localization and Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or embedded maps) and can not adapt well to temporarily changed drivable areas such as work zones. Navigating CAVs in such areas heavily relies on how the vehicle defines drivable areas based on perception information. Difficulties in improving perception accuracy and ensuring the correct interpretation of perception results are challenging to the vehicle in these situations. This paper presents a prototype that introduces crowdsourcing trajectories information into the mapping process to enhance CAV's understanding on the drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to construct the temporarily changed drivable area and occupancy grid map (OGM) based on crowdsourcing trajectories. The proposed method is compared with SLAM without any human driving information. Our method has adapted well with the downstream path planning and vehicle control module, and the CAV did not violate driving rule, which a pure SLAM method did not achieve.

Related papers

• Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data [12.853720506838043]
  Autonomous vehicles commonly rely on High Definition (HD) maps that contain information about the assignment of traffic lights to lanes. To remedy these issues, our novel approach derives the assignments from traffic light states and the corresponding motion patterns of vehicle traffic. Our publicly available API for the Lyft Level 5 dataset enables researchers to develop and evaluate their own approaches.
  arXiv  Detail & Related papers  (2023-09-26T09:42:21Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task [0.0]
  We propose a CNN-based method that can leverage the steering wheel angle information to improve the road area semantic segmentation. We demonstrate the effectiveness of the proposed approach on two challenging data sets for autonomous driving.
  arXiv  Detail & Related papers  (2022-12-19T13:25:09Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• Geolocation estimation of target vehicles using image processing and geometric computation [9.581332581510184]
  Estimating vehicles' locations is one of the key components in intelligent traffic management systems. The development of advanced sensing and communication technologies on modern vehicles makes it feasible to use such vehicles to estimate the traffic data of observed vehicles. We propose a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem.
  arXiv  Detail & Related papers  (2022-03-08T13:15:29Z)
• Trajectory Prediction for Autonomous Driving with Topometric Map [10.831436392239585]
  State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. We propose an end-to-end transformer networks based approach for map-less autonomous driving.
  arXiv  Detail & Related papers  (2021-05-09T08:16:16Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
  We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
  arXiv  Detail & Related papers  (2021-04-19T11:48:13Z)
• LiveMap: Real-Time Dynamic Map in Automotive Edge Computing [14.195521569220448]
  LiveMap is a real-time dynamic map that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second. We develop the control plane of LiveMap that allows adaptive offloading of vehicle computations. We implement LiveMap on a small-scale testbed and develop a large-scale network simulator.
  arXiv  Detail & Related papers  (2020-12-16T15:00:49Z)
• BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments [54.22535063244038]
  We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments. Our method is designed for unstructured real-world scenarios with dense and heterogeneous traffic consisting of cars, trucks, two-and three-wheelers, and pedestrians.
  arXiv  Detail & Related papers  (2020-09-22T08:25:44Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)

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.