Technology peripherals
AI
World's largest self-driving test begins next week, deploying 100 vehicles
World's largest self-driving test begins next week, deploying 100 vehicles
News on November 11, Teslarati reported that the world’s largest self-driving car test, conducted by Vanderbilt University and several other universities in cooperation with Nissan, Toyota, General Motors and the Tennessee Department of Transportation, will Taking place next week.
Autonomous driving is one of the most important technologies in the automotive industry, but safety is also a problem that this technology must solve. So to test the impact of self-driving cars on real-world driving, a group of legacy automakers are taking part in the world's largest self-driving test on a stretch of highway near Nashville, Tennessee. Researchers will observe the impact of self-driving on traffic rhythm and try to determine whether self-driving cars can reduce human-caused traffic congestion.
According to Vanderbilt University, the self-driving test will be conducted under strict restrictions. The university will deploy 100 self-driving vehicles on a 4-mile stretch of I-24, with testing taking place between 5 a.m. and 10:30 a.m. Test vehicles include a Nissan Rouge, Toyota RAV4 and Cadillac XT5, each equipped with self-driving technology.
The university conducted a small-scale test earlier this year on a test section with a total of 20 vehicles. The test results found that one self-driving car can affect the driving speed of the entire group. They will now investigate whether the above preliminary results can be replicated in the real world. The researchers will also observe whether the improvements in average fuel economy seen in smaller-scale testing are reproduced.
A stretch of I-24 in Nashville was chosen as the testing site for a specific reason. The stretch of highway, named the "I-24 MOTION Testbed," is equipped with 300 4K cameras placed on poles spaced 600 feet (approximately 183 meters) along the stretch of highway to collect the average speed and speed of traffic. Other statistics, and can provide a very good look at individual self-driving cars and how they interact with other vehicles.
Automakers will use the test to see how their own (and competitors) vehicles perform in the real world, perhaps leading to advancements in self-driving.
The above is the detailed content of World's largest self-driving test begins next week, deploying 100 vehicles. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1386
52
Why is Gaussian Splatting so popular in autonomous driving that NeRF is starting to be abandoned?
Jan 17, 2024 pm 02:57 PM
Written above & the author’s personal understanding Three-dimensional Gaussiansplatting (3DGS) is a transformative technology that has emerged in the fields of explicit radiation fields and computer graphics in recent years. This innovative method is characterized by the use of millions of 3D Gaussians, which is very different from the neural radiation field (NeRF) method, which mainly uses an implicit coordinate-based model to map spatial coordinates to pixel values. With its explicit scene representation and differentiable rendering algorithms, 3DGS not only guarantees real-time rendering capabilities, but also introduces an unprecedented level of control and scene editing. This positions 3DGS as a potential game-changer for next-generation 3D reconstruction and representation. To this end, we provide a systematic overview of the latest developments and concerns in the field of 3DGS for the first time.
How to solve the long tail problem in autonomous driving scenarios?
Jun 02, 2024 pm 02:44 PM
Yesterday during the interview, I was asked whether I had done any long-tail related questions, so I thought I would give a brief summary. The long-tail problem of autonomous driving refers to edge cases in autonomous vehicles, that is, possible scenarios with a low probability of occurrence. The perceived long-tail problem is one of the main reasons currently limiting the operational design domain of single-vehicle intelligent autonomous vehicles. The underlying architecture and most technical issues of autonomous driving have been solved, and the remaining 5% of long-tail problems have gradually become the key to restricting the development of autonomous driving. These problems include a variety of fragmented scenarios, extreme situations, and unpredictable human behavior. The "long tail" of edge scenarios in autonomous driving refers to edge cases in autonomous vehicles (AVs). Edge cases are possible scenarios with a low probability of occurrence. these rare events
Choose camera or lidar? A recent review on achieving robust 3D object detection
Jan 26, 2024 am 11:18 AM
0.Written in front&& Personal understanding that autonomous driving systems rely on advanced perception, decision-making and control technologies, by using various sensors (such as cameras, lidar, radar, etc.) to perceive the surrounding environment, and using algorithms and models for real-time analysis and decision-making. This enables vehicles to recognize road signs, detect and track other vehicles, predict pedestrian behavior, etc., thereby safely operating and adapting to complex traffic environments. This technology is currently attracting widespread attention and is considered an important development area in the future of transportation. one. But what makes autonomous driving difficult is figuring out how to make the car understand what's going on around it. This requires that the three-dimensional object detection algorithm in the autonomous driving system can accurately perceive and describe objects in the surrounding environment, including their locations,
Have you really mastered coordinate system conversion? Multi-sensor issues that are inseparable from autonomous driving
Oct 12, 2023 am 11:21 AM
The first pilot and key article mainly introduces several commonly used coordinate systems in autonomous driving technology, and how to complete the correlation and conversion between them, and finally build a unified environment model. The focus here is to understand the conversion from vehicle to camera rigid body (external parameters), camera to image conversion (internal parameters), and image to pixel unit conversion. The conversion from 3D to 2D will have corresponding distortion, translation, etc. Key points: The vehicle coordinate system and the camera body coordinate system need to be rewritten: the plane coordinate system and the pixel coordinate system. Difficulty: image distortion must be considered. Both de-distortion and distortion addition are compensated on the image plane. 2. Introduction There are four vision systems in total. Coordinate system: pixel plane coordinate system (u, v), image coordinate system (x, y), camera coordinate system () and world coordinate system (). There is a relationship between each coordinate system,
This article is enough for you to read about autonomous driving and trajectory prediction!
Feb 28, 2024 pm 07:20 PM
Trajectory prediction plays an important role in autonomous driving. Autonomous driving trajectory prediction refers to predicting the future driving trajectory of the vehicle by analyzing various data during the vehicle's driving process. As the core module of autonomous driving, the quality of trajectory prediction is crucial to downstream planning control. The trajectory prediction task has a rich technology stack and requires familiarity with autonomous driving dynamic/static perception, high-precision maps, lane lines, neural network architecture (CNN&GNN&Transformer) skills, etc. It is very difficult to get started! Many fans hope to get started with trajectory prediction as soon as possible and avoid pitfalls. Today I will take stock of some common problems and introductory learning methods for trajectory prediction! Introductory related knowledge 1. Are the preview papers in order? A: Look at the survey first, p
SIMPL: A simple and efficient multi-agent motion prediction benchmark for autonomous driving
Feb 20, 2024 am 11:48 AM
Original title: SIMPL: ASimpleandEfficientMulti-agentMotionPredictionBaselineforAutonomousDriving Paper link: https://arxiv.org/pdf/2402.02519.pdf Code link: https://github.com/HKUST-Aerial-Robotics/SIMPL Author unit: Hong Kong University of Science and Technology DJI Paper idea: This paper proposes a simple and efficient motion prediction baseline (SIMPL) for autonomous vehicles. Compared with traditional agent-cent
Let's talk about end-to-end and next-generation autonomous driving systems, as well as some misunderstandings about end-to-end autonomous driving?
Apr 15, 2024 pm 04:13 PM
In the past month, due to some well-known reasons, I have had very intensive exchanges with various teachers and classmates in the industry. An inevitable topic in the exchange is naturally end-to-end and the popular Tesla FSDV12. I would like to take this opportunity to sort out some of my thoughts and opinions at this moment for your reference and discussion. How to define an end-to-end autonomous driving system, and what problems should be expected to be solved end-to-end? According to the most traditional definition, an end-to-end system refers to a system that inputs raw information from sensors and directly outputs variables of concern to the task. For example, in image recognition, CNN can be called end-to-end compared to the traditional feature extractor + classifier method. In autonomous driving tasks, input data from various sensors (camera/LiDAR
nuScenes' latest SOTA | SparseAD: Sparse query helps efficient end-to-end autonomous driving!
Apr 17, 2024 pm 06:22 PM
Written in front & starting point The end-to-end paradigm uses a unified framework to achieve multi-tasking in autonomous driving systems. Despite the simplicity and clarity of this paradigm, the performance of end-to-end autonomous driving methods on subtasks still lags far behind single-task methods. At the same time, the dense bird's-eye view (BEV) features widely used in previous end-to-end methods make it difficult to scale to more modalities or tasks. A sparse search-centric end-to-end autonomous driving paradigm (SparseAD) is proposed here, in which sparse search fully represents the entire driving scenario, including space, time, and tasks, without any dense BEV representation. Specifically, a unified sparse architecture is designed for task awareness including detection, tracking, and online mapping. In addition, heavy
