A Situation-Adaptive Lane-Keeping Support System: Overview of the SAFELANE Approach

Abstract

Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.

Research on effects of pattern, amplitude and frequency of pulse steering torque warnings for lane departure

“…In order to trigger a timely warning at low false-alarm rate, Time to Lane Crossing (TLC) (Amditis et al, 2010;Mammar, Glaser, & Netto, 2006) and Distance to Lane Center (DLC) joint decision-making algorithm is adopted. When steering torque applied by driver is lower than 2 N m, and the vehicle speed is above 65 km/h, if any of the following conditions are satisfied, warning will be triggered: |DLC| > 0.75 m. TLC < T th1 for two consecutive samples, where T th1 is the threshold to trigger warnings.…”

The Driving School System: Learning Basic Driving Skills From a Teacher in a Real Car

“…Here, t denotes the time of the image observation. 3 Because we found that only a description of the street ahead is not sufficient to distinguish different driving situations from each other, e.g., a straight street can lead to acceleration or no acceleration, depending on the current velocity, we also store a short sequence of previous steering (s past ). We use a fixed number of m = 50 discrete steering actions that preceded I t .…”

Minimizing the Disruption of Traffic Flow of Automated Vehicles During Lane Changes

“…Some work focuses on lane change assistant systems for human drivers [4,6,7], while others consider lane change collision avoidance systems [8][9][10][11][12]. Chee and Tomizuka studied the lane change maneuver that is most comfortable to passengers [13,14].…”