


He Xiaopeng: The complete logic of fully driverless driving is still unclear, and we may need to find another way
News from this site on November 21st. Tonight, He Xiaopeng, chairman of Xpeng Motors, expressed his opinion on Weibo, saying, “You can see a clear road map for high-level autonomous driving assistance or fully autonomous driving, but there is no clear road map. Human drivers still can’t see the complete logic, and I even think that we may need to find another way.”

He Xiaopeng forwarded a message about Cruise Chief Executive Officer Reports of executive Kyle Vogt's resignation. Vogt did not explain the reason for his resignation, but cited an incident that led to the suspension of self-driving car operations and a safety review.
On the evening of October 2, a woman was violently hit by a car in San Francisco. , fell in front of a Cruise driverless taxi. Although the Cruise self-driving taxi braked in time, it then tried to pull over, causing the woman to be run over a second time and dragged about 6 meters, causing her injuries to become more serious. The California Department of Motor Vehicles suspended Cruise’s driverless taxi operating license on October 25. On November 7, Cruise announced the recall of 950 self-driving cars in the United States, and may continue to recall more vehicles

This site noticed that small The first batch of Peng XNGP urban navigation assisted driving without high-precision maps has begun public testing. XNGP urban smart driving adopts a light map solution, so that urban navigation is no longer limited by the use scope and update time of high-precision maps. After enabling AI driving, this function allows users to set starting and ending points across the country. Just drive it manually once to generate a memory map. When you select the travel route later, you can use the "AI driving" function to realize urban navigation assisted driving on specific routes or specific scenarios
Previously, Xpeng Motors has announced XNGP urban navigation assisted driving in November Covering 25 cities, it will be open to 50 cities by the end of December. He Xiaopengli expressed the challenge to achieve full coverage of XNGP in major urban road networks (including Class 1-4 roads) across the country within 2024.
Advertising statement: The external jump links (including but not limited to hyperlinks, QR codes, passwords, etc.) contained in the article are used to convey more information and save selection time. The results are for reference only. All articles on the site contain this statement.
The above is the detailed content of He Xiaopeng: The complete logic of fully driverless driving is still unclear, and we may need to find another way. 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

AI Hentai Generator
Generate AI Hentai for free.

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



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.

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

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,

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

According to news on December 15, Xpeng Motors has just released the exciting news that they plan to officially launch their first MPV model on January 1, 2024, called Xpeng X9. This car is officially hailed as a "super smart driving seven-seater" and boasts that it has "AI deep evolution to explore X possibilities". The Xpeng X9 made its debut at the 2023 Guangzhou Auto Show, with pre-sale prices starting from 388,000 yuan. This car continues the Xpeng Motors family-style design, with a through-type light strip and sharp headlight design. The chassis adopts front and rear integrated aluminum die-casting. The body dimensions are 5293mm long, 1988mm wide, 1785mm high, and the wheelbase is 3160mm. The interior decoration of the car provides three different color schemes: Qiyu Gray, Moon Shadow Coffee, and Starry Night Black. Front seat

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

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

Target detection is a relatively mature problem in autonomous driving systems, among which pedestrian detection is one of the earliest algorithms to be deployed. Very comprehensive research has been carried out in most papers. However, distance perception using fisheye cameras for surround view is relatively less studied. Due to large radial distortion, standard bounding box representation is difficult to implement in fisheye cameras. To alleviate the above description, we explore extended bounding box, ellipse, and general polygon designs into polar/angular representations and define an instance segmentation mIOU metric to analyze these representations. The proposed model fisheyeDetNet with polygonal shape outperforms other models and simultaneously achieves 49.5% mAP on the Valeo fisheye camera dataset for autonomous driving
