Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

280
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
280
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

622
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
622
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

37
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
37
Uncertainty: Overview00:59

Uncertainty: Overview

498
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
498
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

455
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
455

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Embryonic arrest at midgestation and disruption of Notch signaling produced by the absence of both epsin 1 and epsin 2 in mice.

Proceedings of the National Academy of Sciences of the United States of America·2009
Same author

Two novel SNPs in coding region of the caprine Fat-inducing transcript gene and their association with growth traits.

Molecular biology reports·2009
Same author

Reprogramming human fibroblasts using HIV-1 TAT recombinant proteins OCT4, SOX2, KLF4 and c-MYC.

Molecular biology reports·2009
Same author

Bioinformatics and microarray analysis of microRNA expression profiles of murine embryonic stem cells, neural stem cells induced from ESCs and isolated from E8.5 mouse neural tube.

Neurological research·2009
Same author

Attenuation of lipopolysaccharide-mediated left ventricular dysfunction by glutamine preconditioning.

The Journal of surgical research·2009
Same author

Differential regulation of Apak by various DNA damage signals.

Molecular and cellular biochemistry·2009
Same journal

Differential sensitivity of self-reported driving and collision measures to aspects of shiftwork, sleep, and fatigue.

Accident; analysis and prevention·2026
Same journal

Delving into the visual attention of pedestrians during street crossing under time pressure: An eye-tracking approach.

Accident; analysis and prevention·2026
Same journal

Differentiating high-frequency and high-severity hotspots: A robust risk-evolution-volume (REV) framework.

Accident; analysis and prevention·2026
Same journal

Modeling takeover decisions in driving automation: a multilevel drift-diffusion model (MDDM) framework integrating human, system, and environmental factors.

Accident; analysis and prevention·2026
Same journal

The state-dependent causal effect of a V2X-based beyond-line-of-sight warning system on young driver response: a causal machine learning approach.

Accident; analysis and prevention·2026
Same journal

How conservative driving behavior increases crash risk: Understanding the systemic safety impacts of older drivers in mixed traffic flows.

Accident; analysis and prevention·2026
See all related articles

Related Experiment Video

Updated: May 27, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K

A safe self-evolution algorithm for autonomous driving based on data-driven risk quantification model.

Shuo Yang1, Shizhen Li1, Yanjun Huang2

  • 1School of Automotive studies, Tongji University, Shanghai, 201804, China.

Accident; Analysis and Prevention
|February 15, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a safe self-evolution algorithm for autonomous driving. It enhances safety in complex traffic by quantifying risks and integrating adjustable safety limits, ensuring safe exploration without sacrificing performance.

Keywords:
Autonomous drivingRisk quantificationSimulationTrajectory planning

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

475

Related Experiment Videos

Last Updated: May 27, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.4K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

475

Area of Science:

  • Artificial Intelligence
  • Robotics
  • Computer Science

Background:

  • Autonomous driving systems aim for independent evolution in complex environments.
  • Ensuring safety during exploration in dynamic traffic is challenging due to the safety-performance trade-off in evolutionary algorithms.

Purpose of the Study:

  • To propose a safe self-evolution algorithm for autonomous driving.
  • To address the challenge of safe exploration without compromising performance in dynamic traffic scenarios.

Main Methods:

  • Developed a data-driven risk quantification model using an attention mechanism, mimicking human risk perception.
  • Integrated this model into a safety-evolutionary decision-control algorithm with adjustable safety limits.

Main Results:

  • The proposed algorithm effectively estimates surrounding environmental risks.
  • Demonstrated safe and reasonable action generation in complex scenarios through simulations and real-vehicle experiments.

Conclusions:

  • The novel algorithm guarantees safety in autonomous driving systems.
  • Maintains the evolutionary potential of learning-based systems while ensuring safe operation.