Inferring the Driver’s Lane Change Intention through LiDAR-Based Environment Analysis Using Convolutional Neural Networks

Abstract

Most of the tactic manoeuvres during driving require a certain understanding of the surrounding environment from which to devise our future behaviour. In this paper, a Convolutional Neural Network (CNN) approach is used to model the lane change behaviour to identify when a driver is going to perform this manoeuvre. To that end, a slightly modified CNN architecture adapted to both spatial (i.e., surrounding environment) and non-spatial (i.e., rest of variables such as relative speed to the front vehicle) input variables. Anticipating a driver’s lane change intention means it is possible to use this information as a new source of data in wide range of different scenarios. One example of such scenarios might be the decision making process support for human drivers through Advanced Driver Assistance Systems (ADAS) fed with the data of the surrounding cars in an inter-vehicular network. Another example might even be its use in autonomous vehicles by using the data of a specific driver profile to make automated driving more human-like. Several CNN architectures have been tested on a simulation environment to assess their performance. Results show that the selected architecture provides a higher degree of accuracy than random guessing (i.e., assigning a class randomly for each observation in the data set), and it can capture subtle differences in behaviour between different driving profiles.

Demand-Driven Data Acquisition for Large Scale Fleets

“…3 Data can be transferred from the vehicle to an external system and persisted there. 4 The external system continuously provides the received sensor data to other systems via streaming. 5 Data transmission can be made dependent on a condition evaluated on each vehicle locally.…”

“…Besides the commercial demand identified in the insurance market, academia also has many active fields of research that depend on vehicle data. Among these fields are driver behavior identification [3], inference of lane change intentions [4], or drowsiness detection [5]. In addition, access to vehicle data is essential for municipalities to facilitate the transition to Smart Cities [6].…”

Auto-Refining 3D Mesh Reconstruction Algorithm From Limited Angle Depth Data

“…Real-world applications for both depth sensors and object reconstruction are numerous, they range from medical applications such as posture recognition [10]- [12], lymphedema intervention [13], respiration abnormality tracking [14], identification of breast cancer [15], to various forms of robotics [16], [17] such as collision avoidance for autonomous vehicles [18], [19] or even entertainment, for example avoiding tripping over obstacles in the real world when in virtual reality [20], [21], reconstruction of environments in augmented reality [22] and improving experience of extended reality application [23]. While the 3D object reconstruction can benefit various fields, one of the main drawbacks when it comes to three-dimensional reconstruction is the overhead for full object reconstruction, this generally involves having an array of calibrated sensors or having the entire filming setup (or the 3D object itself) rotate for the entirety of the object to be fully scanned.…”

HUMANNET—A Two-Tiered Deep Neural Network Architecture for Self-Occluding Humanoid Pose Reconstruction

“…A lot of applications that would benefit from real-time object reconstruction such as self-driving cars [5,6], interactive medium particularly virtual reality [7] (VR) and video games, augmented reality [8] (AR) and extended reality [9] (XR). Furthermore, depth sensor information can improve gesture [10,11] and posture recognition [12] technologies as these tasks generally have a lot of important depth information embedded into them.…”