In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently aggregating the information received from multiple nearby vehicles, we can observe the same scene from different viewpoints. This allows us to see through occlusions and detect actors at long range, where the observations are very sparse or non-existent. We also show that our approach of sending compressed deep feature map activations achieves high accuracy while satisfying communication bandwidth requirements.

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.

Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation

Predicting turn and stop maneuvers of potentially errant drivers is a basic requirement for advanced driver assistance systems for urban intersections. Previous work has shown that an early estimate of the driver's intent can be inferred by evaluating the vehicle's speed during the intersection approach. In the presence of a preceding vehicle, however, the velocity profile might be dictated by car-following behaviour rather than by the need to slow down before doing a left or right turn. To infer the driver's intent under such circumstances, a simple, real-time capable approach using an explicit model to represent both car-following and turning behaviour is proposed. Models for typical turning behavior are extracted from real world data. Preliminary results based on a Bayes net classification are presented.

Driver intent inference at urban intersections using the intelligent driver model

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust cooperative perception framework with V2X communication using a novel vision Transformer. Specifically, we build a holistic attention model, namely V2X-ViT, to effectively fuse information across on-road agents (i.e., vehicles and infrastructure). V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention, which captures inter-agent interaction and per-agent spatial relationships. These key modules are designed in a unified Transformer architecture to handle common V2X challenges, including asynchronous information sharing, pose errors, and heterogeneity of V2X components. To validate our approach, we create a large-scale V2X perception dataset using CARLA and OpenCDA. Extensive experimental results demonstrate that V2X-ViT sets new state-of-the-art performance for 3D object detection and achieves robust performance even under harsh, noisy environments. The code is available at https://github.com/DerrickXuNu/v2x-vit.

pdf/v2x-vit-vehicle-to-everything-cooperative-perception-with-16vxu808.pdf

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

Autonomous vehicles are heavily reliant upon their sensors to perfect the perception of surrounding environments, however, with the current state of technology, the data which a vehicle uses is confined to that from its own sensors. Data sharing between vehicles and/or edge servers is limited by the available network bandwidth and the stringent real-time constraints of autonomous driving applications. To address these issues, we propose a point cloud feature based cooperative perception framework (F-Cooper) for connected autonomous vehicles to achieve a better object detection precision. Not only will feature based data be sufficient for the training process, we also use the features' intrinsically small size to achieve real-time edge computing, without running the risk of congesting the network. Our experiment results show that by fusing features, we are able to achieve a better object detection result, around 10% improvement for detection within 20 meters and 30% for further distances, as well as achieve faster edge computing with a low communication delay, requiring 71 milliseconds in certain feature selections. To the best of our knowledge, we are the first to introduce feature-level data fusion to connected autonomous vehicles for the purpose of enhancing object detection and making real-time edge computing on inter-vehicle data feasible for autonomous vehicles.

https://dl.acm.org/doi/pdf/10.1145/3318216.3363300

F-cooper: feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds

In cooperative perception systems, different vehicles share object data obtained by their local environment perception sensors, like radar or lidar, via wireless communication. In this paper, this so-called Car2X-based perception is modeled as a virtual sensor in order to integrate it into a highlevel sensor data fusion architecture. The spatial and temporal alignment of incoming data is a major issue in cooperative perception systems. Temporal alignment is done by predicting the received object data with a model-based approach. In this context, the CTRA (constant turn rate and acceleration) motion model is used for a three-dimensional prediction of the communication partner's motion. Concerning the spatial alignment, two approaches to transform the received data, including the uncertainties, into the receiving vehicle's local coordinate frame are compared. The approach using an unscented transformation is shown to be superior to the approach by linearizing the transformation function. Experimental results prove the accuracy and consistency of the virtual sensor's output.

Car2X-based perception in a high-level fusion architecture for cooperative perception systems

In cooperative perception systems, different vehicles share object data obtained by their local environment perception sensors, like radar or lidar, via wireless communication. In this paper, a fusion architecture for a cooperative perception system and a concept for a parametrizable offline simulation are proposed. In order to effectively develop and simulate such systems, knowledge about the main parameters of the employed wireless communication solution is crucial. For this reason, an experimental analysis of parameters like transmission latencies and transmission range of a communication solution based on IEEE 802.11p is presented.

Analysis of V2X communication parameters for the development of a fusion architecture for cooperative perception systems

Virtual testing can improve safety and quality of driver assistance systems. However, for the accuracy of the whole simulation, the realism of the virtual sensors is crucial. This paper describes a method for simulating an automotive FMCW radar from the wave transmission up to the intermediate frequency signal. The wave propagation is simulated using geometrical optics and ray launching. To obtain high fidelity, antenna power patterns of transmitter and receiver, reflection, and ray divergence due to curved surfaces are included. Moreover the model inherently includes interference, limited resolution and energy conservation. In contrast to most previous work, our model is deployed in the detailed environment of a driving simulator. Finally, the implementation of the ray launcher using the GPU accelerated OptiX ray tracing engine proves real-time performance and comparable results to recorded measurements.

A ray launching approach for modeling an FMCW radar system

In cooperative perception systems, different vehicles share object data obtained by their local environment perception sensors, like radar or lidar, via wireless communication. Inaccurate self-localizations of the vehicles complicate association of locally perceived objects and objects detected and transmitted by other vehicles. In this paper, a method for inter-vehicle object association is presented. Position and orientation offsets between object lists from different vehicles are estimated by applying point matching algorithms. Different algorithms are analyzed in simulations concerning their robustness and performance. Results with a first implementation of the so-called Auction-ICP algorithm in a real test vehicle validate the simulation results.

Inter-vehicle object association for cooperative perception systems

With rising complexity of advanced driver assistance systems (ADAS), development quality and speed are of increasing importance. Virtualization contributes towards this goal by providing a controlled environment even before hardware prototypes are available. One crucial component is a realistic description of the vehicle's sensors by means of sensor models. We propose a generic, modular architecture for the design of such models. The car's surroundings are represented by a list of objects that is modified during the virtualized sensing process. The result can then provide stimulation of ADAS function algorithms. Using the proposed architecture as a framework, we construct an exemplary model employing a stochastic variation of target object positions.

Generic architecture for simulation of ADAS sensors

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Andreas Rauch

Author Tools

Chat about Author