Provident vehicle detection at night for advanced driver assistance systems

Abstract

In recent years, computer vision algorithms have become more powerful, which enabled technologies such as autonomous driving to evolve rapidly. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently (denoted as provident vehicle detection). For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively (react before the oncoming vehicle is directly visible). Using this experimental setting, the provident vehicle detection system’s time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently (detection based on observable visual cues the objects cause before they are visible) and further close the performance gap between human behavior and computer vision algorithms to bring autonomous and automated driving a step forward.

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

“…Kuang et al [111] achieved a high detection rate of 95.82% with minimal false positives in their multiclass system for nighttime conditions, employing tensor decomposition and object proposal methods. Furthermore, the research in [112] presents a system that integrates rule-based algorithms with a shallow neural network for nighttime vehicle detection, demonstrating significant improvements over traditional methods in terms of detection speed and proactive capabilities. These studies highlight the strengths of both traditional and hybrid approaches in the realm of nighttime vehicle detection, providing a foundation for future advancements in this field.…”

A review of automotive intelligent and adaptive headlight beams intensity control approaches

“…80 Similarly, in 2021, of the eight (8) sampled authors who conducted a study on intelligent headlight beam intensity control and design of intelligent headlight, five (5) authors representing 62.5% used the sensor-based headlight beam intensity control approach, 5,43,81,82 and the remaining three (3) authors representing 37.5% used the machinelearning-based intensity control approach in the design of the intelligent headlight. 31,83,84 In 2022 out of the ten (10) sampled authors who conducted a study into the design of intelligent headlights, seven (7) authors representing 70% adopted the machine-learning-based headlight beam intensity control approach, 75,83,[85][86][87][88] , two (2) authors representing 20% utilized the sensor-based headlight beam intensity control approach, 40 and the remaining one (1) author representing 10% used the pulse width modulation approach for the design of the intelligent headlight. 89 Figure 4 illustrates that the predominant approaches for controlling intelligent headlight beams are machinelearning-based and sensor-based intensity control methods.…”