Abstract: Methods, systems, and computer programs are presented for monitoring tailgating when a vehicle follows another vehicle at an unsafe distance. A method for enhancing a Following Distance (FD) machine learning (ML) model is disclosed. The method includes providing a management user interface (UI) for configuring FD parameters, followed by receiving FD events. A UI for manual FD annotation and another for customer review of filtered FD events are also provided. Annotations and customer review information are collected to improve the training set for the FD ML model. The FD model is then trained with the new data and downloaded to a vehicle. Once installed, the FD model is utilized to detect FD events within the vehicle, thereby enhancing the vehicle's safety and performance in driving scenarios by improving the accuracy and reliability of FD event predictions or detections.

Abstract: Methods, systems, and computer programs are presented for the management of lane-departure (LD) events. One method includes training a classifier for LD events and loading the classifier into a vehicle. LD events are detected based on outward images using the classifier, while the turn signal is monitored to prevent false triggers. If an LD event is detected, rules are checked for alerting the driver and deciding whether to alert the driver or not. Subsequently, additional rules are checked for reporting the event and deciding whether to report the event to a Behavior Monitoring System (BMS) or to discard it. The method also includes a solid line departure model that identifies crossing dashed, solid-white, and solid-yellow lanes, delaying alerts and event generation until a significant portion of the vehicle crosses over the lane. The model also outputs a confidence score reflecting the amount of vehicle deviation from the driving lane.