Objective: Determine whether the size-arrival effect (SAE) occurs with immersive, 3D visual experiences and active collision-avoidance responses.

Background: When a small near object and a large far object approach the observer at the same speeds, the large object appears to arrive before the small object, known as the size-arrival effect (SAE), which may contribute to crashes between motorcycles and cars. Prior studies of the SAE were limited because they used two dimensional displays and asked participants to make passive judgments.

Method: Participants viewed approaching objects using a virtual reality (VR) headset. In an active task, participants ducked before the object hit them. In a passive prediction-motion (PM) judgment, the approaching object disappeared, and participants pressed a button when they thought the object would hit them. In a passive relative TTC judgment, participants reported which of two approaching objects would reach them first.

Results: The SAE occurred with the PM and relative TTC tasks but not with the ducking task. The SAE can occur in immersive 3D environments but is limited by the nature of the task and display.

Application: Certain traffic situations may be more prone to the SAE and have higher risk for collisions. For example, in left-turn scenarios (e.g., see Levulis, 2018), drivers make passive judgments when oncoming vehicles are far and optical expansion is slow, and binocular disparity putatively is ineffective. Collision-avoidance warning systems may be needed more in such scenarios than when vehicles are near and drivers' judgments of TTC may be more accurate (DeLucia, 2008).

Download full-text PDF

Source
http://dx.doi.org/10.1177/00187208211031043DOI Listing

Publication Analysis

Top Keywords

active collision-avoidance
8
virtual reality
8
size-arrival sae
8
small object
8
large object
8
approaching objects
8
object hit
8
hit passive
8
relative ttc
8
object
7

Similar Publications

Navigating public environments requires adjustments to one's walking patterns to avoid stationary and moving obstacles. It is known that physical inactivity induces alterations in motor capacities, but the impact of inactivity on anticipatory locomotor adjustments (ALA) has not been studied. The purpose of the present exploratory study was to compare ALAs and related muscle co-contraction during a pedestrian circumvention task between active (AA) and inactive young adults (IA).

View Article and Find Full Text PDF

Activation strategies and effectiveness of Intelligent safety systems for reducing pedestrian injuries in autonomous vehicles.

Accid Anal Prev

December 2024

School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China. Electronic address:

Intelligent safety systems (ISS) for autonomous vehicles, integrating advanced perception capabilities and passive protection devices, are expected to reshape traditional pedestrian safety systems and play a key role in reducing the risk of pedestrian injuries in traffic accidents. However, traditional active control and passive protection modules remain disconnected due to insufficient evidence supporting the effectiveness of collaborative strategies in integrated systems, particularly concerning activation criteria and timing. This study aims to address this gap by developing a comprehensive ISS that incorporates advanced perception systems, a vehicle dynamic control module, and controllable passive safety devices.

View Article and Find Full Text PDF

A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition.

Biomimetics (Basel)

October 2024

Machine Life and Intelligence Research Centre, School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, China.

Animals utilize their well-evolved dynamic vision systems to perceive and evade collision threats. Driven by biological research, bio-inspired models based on lobula giant movement detectors (LGMDs) address certain gaps in constructing artificial collision-detecting vision systems with robust selectivity, offering reliable, low-cost, and miniaturized collision sensors across various scenes. Recent progress in neuroscience has revealed the energetic advantages of dendritic arrangements presynaptic to the LGMDs, which receive contrast polarity-specific signals on separate dendritic fields.

View Article and Find Full Text PDF
Article Synopsis
  • The study investigates how sport-specific training enhances athletes' collision avoidance skills in dynamic environments, particularly in a virtual reality setting.
  • It compares responses of trained athletes to untrained young adults while they navigate a path and avoid virtual opponents approaching at various speeds.
  • Results indicate that athletes excel in attention-demanding tasks and show adaptive behaviors, but no significant differences were found in their actual collision avoidance timing or clearance.
View Article and Find Full Text PDF

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC.

Int J Rob Res

November 2024

Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Article Synopsis
  • Dynamical system-based motion planning allows for smooth, collision-free movement while responding to obstacles in real time through analytical expressions.
  • State-of-the-art approaches can struggle with local minima from complex, non-convex obstacles, while traditional Model Predictive Control (MPC) suffers from high computational costs in dense spaces.
  • The proposed method modulates joint-space dynamics using sampling-based MPC, which activates only when local minima are detected, enabling robots to effectively navigate cluttered environments and avoid obstacles while maintaining stability.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!