Autonomous Driving Modules

Autonomous driving is a vehicle’s ability to make decisions and take action without direct human control. It is expected to reduce car crashes and increase safety.


However, AVs still have many challenges to overcome before they can produce significant societal cost-savings. Among those challenges are motion sickness, lane departures and road construction.

Adaptive Cruise Control

When you’re on a long road trip, turning on cruise control and kicking back to relax can be the first step in a stress-free driving experience. Most new cars now offer a much more advanced form of cruise control called adaptive c 방문운전연수 ruise control (ACC). Also known as dynamic cruise control or intelligent cruise control, this system uses sensors to track the car ahead and maintain a safe driving gap. If you get close to a car in front of you, the cruise control will automatically slow down or even hit the brakes to prevent a collision. Some systems use multiple types of sensors, and are even more sophisticated in their ability to predict the behavior of nearby vehicles.

ACC can make driving on crowded highways and interstates less stressful, especially during rush hour traffic when speeds can vary greatly from fast to stop-and-go. However, it is important to remember that it doesn’t solve the problem of phantom traffic jams, which are caused by human drivers and not traffic congestion.

Depending on the manufacturer, ACC can use radar equipment mounted under the front grille or bumper, or binocular computer vision systems and stereoscopic cameras. Some systems can also read speed limit signs, and can adjust the vehicle’s speed to match the speed limit of the highway or freeway you are traveling on.

Lane Departu 방문운전연수 re Warning

It’s common for drivers to get distracted and unintentionally drift out of their lane. Lane departure warning, also known as LDW or LDK, works to prevent this by alerting the driver. It uses a camera mounted in the front of the car to monitor the road and identify lane markings. If the car is getting close to or over the lane markers, the system will warn you with beeps and perhaps a visual cue on the dashboard. Some systems use haptic feedback to vibrate the steering wheel or seat, mimicking the feeling of driving on rumble strips.

These systems aren’t foolproof, however. They can fail when the road is wet or covered in snow. Plus, they may be confused if the road’s lane markings are faded or worn out. It’s also important to remember that none of these technologies can replace fully autonomous driving. They’re just assists that help keep your commute safer and more enjoyable.

If you’re interested in learning more about Honda’s safety features, we encourage you to visit us at Tipp City Honda and take a new Honda with Honda Sensing(r) for a test drive. We’ll be happy to answer any questions you have about this impressive suite of driver assist technology. Contact us today to schedule your appointment! We look forward to seeing you soon.

Traffic Sign Recognition

There are a number of different systems on the market that can recognise road traffic signs and notify the driver via the car’s digital display. Typically, this system is powered by advanced forward-facing cameras fitted to the front of the car that scan for and recognise various types of signage on the road.

Most of the time, these systems will recognise speed limit signs, overtaking zones and stop/do not enter/children crossing signs amongst others. Once recognised, these signs are relayed in real-time on the car’s infotainment or instrument cluster screen. This enables the driver to be fully aware of the current driving scenario and to avoid breaking any laws such as exceeding the speed limit or overtaking where it is forbidden, for example.

In most cases, these systems are an integral part of your vehicle’s ADAS package, which also includes lane departure warning technology. As such, they are often used in conjunction with one another to provide you with the best possible driving experience.

A common challenge for these systems is to overcome the problems of illumination, occlusion and distortion that may affect image recognition. However, recent developments in deep learning based object detection algorithms such as RetinaNet have shown that these challenges can be overcome using advanced signal processing and image classification techniques. This allows for high-resolution images with very good performance even under challenging conditions.

Object Detection

Object detection is one of the most important modules for autonomous driving. It is used to identify obstacles or dangers and warn the driver in real-time. It helps the vehicle avoid collision with other cars, pedestrians and other hurdles. It is based on convolutional neural networks (CNN) that have been trained using image data. MATLAB has interactive apps that let you prepare training data and customize object detectors, such as the Image Labeler app and the Video Labeler app.

Unlike tracking, which involves identifying an object’s position in each frame, object detection only deals with the identification of objects at the current point in time. It does not take into account what a particular object might be doing in the future. For example, it does not know that a person walking across a street will be at a different location a few seconds later.

Currently, most autonomous vehicles rely on sensor technologies like radar and LiDAR to detect objects. However, recent developments in deep learning-based vision systems are allowing them to use object detection algorithms with much greater accuracy. Using these advanced algorithms, self-driving cars are able to recognize traffic signs, pedestrians and other obstacles and make decisions that keep them safe and on track. In addition, object detection is also being used to spot hard-to-see items like polyps and lesions during surgery.