A Predictive Model of a Driver’s Target Trajectory Based on Estimated Driving Behaviors

Abstract

With the development of automated driving, inferring a driver’s behavior can be a key element for designing an Advanced Driver Assistance System (ADAS). Current research is focused on describing and predicting a driver’s behaviors as labels, e.g., lane shifting, lane keeping, etc., during driving. In our work, we consider that predicting a driver’s behavior can be described as predicting a trajectory the driver may follow in the near future. The target trajectory can be calculated through certain polynomial functions. Via the data set collected by a Driving Simulator experiment covering nine volunteers, we proposed a model based on a deep learning network which is capable of predicting the corresponding coefficients of polynomial functions and then generating the trajectories in the next few seconds. The results also discussed and analyzed some possible factors affecting the prediction error. In conclusion, the model proved to be effective in predicting the target trajectory of a driver.

An Intelligent System to Sense Textual Cues for Location Assistance in Autonomous Vehicles

“…While driving on the road, AV performs multiple operations such as lane change, lane keeping, overtaking, and following the traffic rules. Several studies proposed and developed numerous methods for ADAS systems [31,32]. It is equally important for an autonomous vehicle to be aware of the textual cues appearing in its outer environment to take some decision or assist the driver, either to stop or drive.…”