Lateral acceleration tests during circular driving maneuvers and double lane change

Probabilistic Driving Models and Lane Change Prediction

Probabilistic traffic models, providing a statistical representation of the future behavior of traffic participants, are crucial for risk estimation in automotive collision avoidance systems. Current research has focused on large-scale behavior, primarily in the form of lane change prediction. These models are limited in their use for high-fidelity driving propagation. This paper investigates a...

Fuzzy Logic Control for Lane Change Maneuvers in Lateral Vehicle Guidance

A Machine Vision Based System for Guiding Lane-change Maneuvers

We propose a new approach for vision based longitudinal and lateral vehicle control which makes extensive use of binocular stereopsis. Longitudinal control i.e. maintaining a safe, constant distance from the vehicle in front is supported by detecting and measuring the distances to leading vehicles using binocular stereo. A known camera geometry with respect to the locally planar road is used to...

Time Series Categorization of Driving Maneuvers Using Acceleration Signals

Two methods of time series analysis were applied to naturalistic driving data. The SAX method reduces the dimensionality of the data by discretizing and quantizing it into distinct symbols. The matrix profile method works on raw data and computes a Euclidian distance measure between subsequences of the time series. Both methods can be used to search for motifs and discords (anomalies) in the da...

PREDICTING DRIVER LANE CHANGE MANEUVERs USING VEHICLE KINEMATIC DATA

One of the challenges of lane departure warning (LDW) systems is to differentiate between normal lane keeping behavior and lane change events in which drivers simply do not use the lane change indicator. Lane keeping behavior differs between drivers and often between driving scenarios, therefore a static threshold of predicting steering maneuver is not an ideal solution. The objective of the cu...