Crafted vs. Learned Representations in Predictive Models - A Case Study on Cyclist Path Prediction

This paper compares two models for context-based path prediction of objects with switching dynamics: a Dynamic Bayesian Network (DBN) and a Recurrent Neural Network (RNN). These models are instances of two larger model categories, distinguished by whether expert knowledge is explicitly crafted into the state representation (and thus is...

Context-based path prediction for targets with switching dynamics

Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this...

Effects of Multisine Signal Bandwidth on Eye Movement Dynamics

In the analysis of human motor skills, tracking tasks with multisine target signals are often performed as they allow for quantitative measurement, identification, and modeling of human control dynamics. In this paper, the same "cybernetic" approach is taken to analyze eye movement dynamics in gaze tracking tasks, where participants had to...

A Unifying Theory of Driver Perception and Steering Control on Straight and Winding Roads

Novel driver support systems potentially enhance road safety by cooperating with the human driver. To optimize the design of emerging steering support systems, a profound understanding of driver steering behavior is required. This article proposes a new theory of driver steering, which unifies visual perception and control models. The theory...

Dual-axis manual control: Performance degradation, axis asymmetry, crossfeed, and intermittency

Vehicle control tasks require simultaneous control of multiple degrees-of-freedom. Most multi-axis human-control modeling is limited to the modeling of multiple fully independent single axes. This paper contributes to the understanding of multi-axis control behavior and draws a more realistic and complete picture of dual-axis manual control....

Effects of Target Signal Shape and System Dynamics on Feedforward in Manual Control

The human controller (HC) in manual control of a dynamical system often follows a visible and predictable reference path (target). The HC can adopt a control strategy combining closed-loop feedback and an open-loop feedforward response. The effects of the target signal waveform shape and the system dynamics on the human feedforward dynamics...

A Cybernetic Analysis of Maximum Unnoticeable Added Dynamics for Different Baseline Controlled Systems

Maximum unnoticeable added dynamics (MUAD) envelopes have been largely used to assess the adequacy of low-order equivalent systems (LOES) in handling qualities assessment and simulator validation. However, research has shown that more thorough verification of the adequacy of this method is required. This paper studies the influence of the...

Using road topology to improve cyclist path prediction

We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected for vehicle egomotion. Tracks are then...

Manual control cybernetics: State-of-the-art and current trends

Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This “cybernetic approach” enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training...

Fundamental issues in manual control cybernetics

Manual control cybernetics aims to understand and describe how humans control vehicles and devices, such that more effective human-machine interfaces can be designed. Current cybernetics theory is primarily based on technology and analysis methods developed in the 1960s and has shown to be limited in its capability to capture the full breadth of...

Constraints in Identification of Multi-Loop Feedforward Human Control Models

The human controller (HC) can greatly improve target-tracking performance by utilizing a feedforward operation on the target signal, in addition to a feedback response. System identification methods are used to determine the correct HC model structure: purely feedback or a combined feedforward/feedback model. In this paper, we investigate three...