Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles

• URL: http://arxiv.org/abs/2104.00169v1
• Date: Thu, 1 Apr 2021 00:12:39 GMT
• Title: Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles
• Authors: Muhyun Back, Jinkyu Lee, Kyuho Bae, Sung Soo Hwang, Il Yong Chun
• Abstract summary: In advanced driver assistant systems and autonomous driving, it is crucial to estimate distances between an ego vehicle and target vehicles. This paper proposes an inter-vehicle distance estimation framework that can consider slope changes of a road forward. Numerical experiments demonstrate that the proposed method significantly improves the distance estimation accuracy and time complexity.
• Score: 14.147976074752023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In advanced driver assistant systems and autonomous driving, it is crucial to estimate distances between an ego vehicle and target vehicles. Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane. In practical driving environments, however, they may drive on different ground planes. This paper proposes an inter-vehicle distance estimation framework that can consider slope changes of a road forward, by estimating road gradients of \emph{both} ego vehicle and target vehicles and using a 2D object detection deep net. Numerical experiments demonstrate that the proposed method significantly improves the distance estimation accuracy and time complexity, compared to deep learning-based depth estimation methods.

Related papers

• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving [59.705635382104454]
  We present Bench2Drive, the first benchmark for evaluating E2E-AD systems' multiple abilities in a closed-loop manner. We implement state-of-the-art E2E-AD models and evaluate them in Bench2Drive, providing insights regarding current status and future directions.
  arXiv  Detail & Related papers  (2024-06-06T09:12:30Z)
• Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA [0.0]
  The goal of this project is to train autonomous vehicles to make decisions to navigate in uncertain environments using deep reinforcement learning techniques. The simulator provides a realistic and urban environment for training and testing self-driving models.
  arXiv  Detail & Related papers  (2023-10-23T04:23:07Z)
• Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios [5.193470362635256]
  Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds. This paper proposes a novel transformer-based trajectory prediction approach, specifically trained to handle any observation length larger than one frame. We perform a comprehensive evaluation of the proposed method using two large-scale highway trajectory datasets.
  arXiv  Detail & Related papers  (2023-06-08T18:03:48Z)
• Learning Position From Vehicle Vibration Using an Inertial Measurement Unit [2.1213500139850012]
  This paper presents a novel approach to vehicle positioning that operates without reliance on the global navigation satellite system (GNSS) Traditional approaches are vulnerable to interference in certain environments, rendering them unreliable in situations such as urban canyons, under flyovers, or in low reception areas. This study proposes a vehicle positioning method based on learning the road signature from accelerometer and gyroscope measurements obtained by an inertial measurement unit (IMU) sensor.
  arXiv  Detail & Related papers  (2023-03-06T18:55:00Z)
• SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
  We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
  arXiv  Detail & Related papers  (2022-04-07T17:58:47Z)
• Multi-Stream Attention Learning for Monocular Vehicle Velocity and Inter-Vehicle Distance Estimation [25.103483428654375]
  Vehicle velocity and inter-vehicle distance estimation are essential for ADAS (Advanced driver-assistance systems) and autonomous vehicles. Recent studies focus on using a low-cost monocular camera to perceive the environment around the vehicle in a data-driven fashion. MSANet is proposed to extract different aspects of features, e.g., spatial and contextual features, for joint vehicle velocity and inter-vehicle distance estimation.
  arXiv  Detail & Related papers  (2021-10-22T06:14:12Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes [69.6715406227469]
  Self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches. We present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework.
  arXiv  Detail & Related papers  (2021-08-10T17:57:03Z)
• End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera [81.66569124029313]
  We propose a camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field.
  arXiv  Detail & Related papers  (2020-06-07T08:18:31Z)
• PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving [8.105493956485583]
  We use a conditional imitation learning algorithm to predict trajectories for ego vehicle and its neighbors. Our approach is computationally efficient and relies only on on-board sensors. We evaluate our method offline on the publicly available dataset nuScenes.
  arXiv  Detail & Related papers  (2020-03-09T16:55:07Z)

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.