- \n
- Safety as the top priority for consumers and manufacturers<\/a><\/li>\n
- Types of vehicle data collected and managed by connected cars<\/a><\/li>\n
- How vehicle data can be applied to enhance car safety<\/a><\/li>\n
- What technology helps OEMs apply vehicle safety data in connected vehicles<\/a><\/li>\n
- Most widely used predictive car maintenance solutions based on vehicle safety data<\/a><\/li>\n
- How IT vendors can help to develop predictive car maintenance solutions<\/a><\/li>\n<\/ul>\n
Safety as the top priority for consumers and manufacturers<\/h2>\n
We\u2019re devoting an increasing proportion of our lives to the road. The average American driver<\/a> spends 18 days a year behind the wheel, while the average driver in the UK<\/a> spends almost four years in a car in their lifetime.<\/p>\n
Therefore, it\u2019s not surprising that car owners are seeking more in-car safety features. According to a European survey<\/a> exploring insights into future car technologies, 40% of respondents in France, Germany, Italy, and the UK consider safety features the most critical.<\/p>\n
Ensuring drivers\u2019 comfort and safety is the main challenge car manufacturers face. Cars have become comfortable cocoons with high-quality sound, supportive seats, and air conditioning. Accidents have become more survivable thanks to innovations such as anti-lock brakes, airbags, and crumple zones.<\/p>\n
It\u2019s vital to mitigate the effects of an accident. But how about preventing one, especially one caused by a vehicle malfunction? Timely diagnostics and maintenance activities can save time, money, and, most importantly, a driver\u2019s health and life.<\/p>\n
As smart technologies evolve, in-car connectivity is becoming essential for automotive safety. Connected cars<\/a> can keep their drivers protected in a variety of ways beyond driving the vehicle autonomously.<\/p>\n
For instance, connected IoT sensors can gather such vehicle data as real-time traffic and crash insights, alerting drivers about road hazards and traffic conditions and suggesting alternative routes. They can also detect inattentive drivers and monitor blind spots.<\/p>\n
Connectivity gets us closer to fully autonomous cars. Let\u2019s explore what technology is under the hood of connected cars.<\/p>\n
What technology helps OEMs apply vehicle safety data in connected vehicles<\/h2>\n
Artificial intelligence<\/h3>\n
The core of in-car connectivity and prediction building is artificial intelligence (AI). By gathering telematics insights and performing vehicle data analytics, AI empowers OEMs \u2014 and their consumers \u2014 with real-time context such as the state of traffic or weather conditions and provides timely alerts related to vehicle maintenance.<\/p>\n
With the help of AI and machine learning (ML) algorithms, numerous vehicle data points thanks to connected IoT sensors, and vehicular communication technologies, OEMs can turn the enormous amount of data they collect into precise alerts and actions that can literally save a driver\u2019s life.<\/p>\n
Internet of Things<\/h3>\n
As it\u2019s possible to collect nearly any kind of data, today\u2019s automakers should consider what big data in automotive is essential and how to apply it to specific business challenges and opportunities. Collecting the right vehicle data is vital, as the number of connected cars grows every month.<\/p>\n
To make the most of telematics data, OEMs leverage IoT technology. With its help, they can track real-time data on vehicle parts and mechanical failures, then continue with the vehicle data analytics and predictive maintenance planning.<\/p>\n
As vehicles become increasingly connected and complex, the amount of data they produce grows. Vehicle data analytics becomes the primary value of IoT. Specifically, automakers can use vehicle safety data processed and analyzed via IoT technology to predict equipment malfunctions, improve the in-car entertainment experience, and assist transport planners in designing safer roadways. Besides, connectivity helps streamline the path from detecting issues to matching drivers with nearby dealer service centers.<\/p>\n
Machine learning<\/h3>\n
Machine learning algorithms are also widely applied to provide OEMs with valuable insights \u2014 in particular, insights related to predictive car maintenance.<\/p>\n
To ensure the accuracy of predictions, it\u2019s essential to train machine learning models using historical and real-time vehicle data. The better an ML model is trained, the more precisely it allows you to monitor equipment health, detect abnormalities, and alert automotive service managers and drivers.<\/p>\n
Specifically, machine learning makes it possible to detect starter malfunctions, predict the end of a battery\u2019s service life, diagnose issues related to steering and braking systems, etc. Once the right vehicle data is collected, it can be sent to the cloud for further analysis and diagnostics.<\/p>\n
Vehicular communication technologies<\/h3>\n
Apart from collecting data from the car itself, it\u2019s also possible to exchange insights with other cars, infrastructure (such as traffic lights), pedestrians, and other entities. Vehicular communication technologies can get information from and share information with anything in the environment that affects the vehicle.<\/p>\n
On top of data sharing technologies is vehicle-to-everything (V2X) communication that shows the most accurate information about a vehicle\u2019s surroundings. With V2X technology, vehicle data from sensors and other sources moves through high-bandwidth, low-latency, and high-reliability links. As V2X technology advances, it\u2019s paving the way to fully autonomous driving, eliminating human error.<\/p>\n
Since entirely self-driving cars that keep drivers 100% safe are still on their way, what are the safety measures OEMs can implement today?<\/p>\n
How vehicle data can be applied to enhance car safety<\/h2>\n
Apart from making predictions in the scenarios described above, there are several more applications where vehicle safety data can help make predictions and prevent maintenance issues.<\/p>\n
Specifically, connected car data<\/a> helps access a real-time stream of diagnostic trouble codes and capture multiple vehicle health indicators to enable more sophisticated correlations between them. Based on connected car diagnostic codes and data, it\u2019s possible to accelerate responses to requests for roadside assistance.<\/p>\n
Besides, vehicle safety data is crucial for better dealership maintenance and implementing over the air (OTA) updates<\/a>. OTA technology allows vehicle owners to update car software or firmware remotely without going to the service center or repair shop. By receiving up-to-date data on traffic flows, road repairs, traffic jams, and weather conditions, drivers can continuously monitor the state of traffic and respond quickly in case of danger or inconvenience.<\/p>\n
Other innovative applications vary from real-time crash prevention systems to anti-theft text alerts when a car is opened\/started. For instance, a collision prevention system<\/a> can warn a driver when a car moves to a different lane or use a slow speed indicator to detect pedestrians. In turn, anti-theft systems<\/a> can track car movements in real time and send alerts when a geofence is breached.<\/p>\n
While data is essential to help drivers foresee road dangers and keep an eye on car parameters, what are the exact types of data car owners and service managers can leverage?<\/p>\n
Types of vehicle data collected by and managed in connected cars<\/h2>\n
In-car connectivity is attached to smart data collected from numerous sources. Depending on the source, there\u2019s in-vehicle data (also called on-board data), sensor data, and external data received from automotive data platforms. Here\u2019s what\u2019s behind these types of data.<\/p>\n
On-board vehicle data<\/h3>\n
On-board data consists of thousands of signals from sensors and engine control units that communicate through a Controller Area Network (CAN).<\/a> These signals are repeatedly sent with a definite frequency and form streams of continuous data. This data is then used for controlling the vehicle and indicating the status of different components.<\/p>\n
The majority of in-vehicle data is technical, indicating parameters such as tire pressure, engine status, vehicle speed, battery charge status, mileage, steering angle, fuel consumption, and outside temperature.<\/p>\n
Types of in-vehicle data<\/b>
\n![\"How](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/predictive-maintenance-info-1.jpg\")
<\/p>\nAll vehicle-generated data is essential for improving the driving experience, increasing the driver\u2019s comfort, contributing to road safety, and reducing fuel consumption.<\/p>\n
On-board data is challenging to acquire, as vehicles are constantly moving. Besides, to ensure effective on-board monitoring or create a predictive car maintenance<\/a> algorithm, manufacturers need powerful hardware: sensors, signals, and computational resources.<\/p>\n
Sensor data<\/h3>\n
There are three main groups of in-car sensor systems that collect sensor data: cameras, radar systems, and Lidar-based systems.<\/p>\n
Data gathered from in-car sensor systems<\/b>
\n![\"How](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/10\/predictive-maintenance-info-2.jpg\")
\nSource: Fierce Electronics<\/a><\/em><\/p>\n- \n
- Camera-based systems.<\/b> This type of system typically includes rear and 360-degree cameras, providing the driver with an image of conditions outside the vehicle and data about the wheel angle. Luxury class cars can also be equipped with cameras projecting virtual 3D image displays.<\/li>\n
- Radar-based systems.<\/b> Radar (radio detection and ranging) sensors use radio waves to collect data. Radar sensors make a crucial contribution to the overall function of autonomous driving. They fulfill several functions: blind spot detection, lane-change assistance, collision warning and avoidance, parking and brake assistance, cross-traffic monitoring, and automatic distance control.<\/li>\n
- Lidar-based systems.<\/b> A relatively new system in the automotive sector, Lidar (light detection and ranging) uses laser pulses to build a 3D model of a car\u2019s environment. Essentially, Lidar helps autonomous vehicles see other objects like cars, pedestrians, and cyclists.<\/li>\n<\/ul>\n
Insights from automotive data platforms<\/h3>\n
Another source of vehicle safety data is platforms that put together information from millions of vehicles. Such platforms typically provide insights into efficient traffic management, route optimization, navigation to parking spots, and estimated arrival times. Besides, automotive data platforms serve a safety function by improving emergency response times, cutting congestion, spotlighting problem areas for transportation planners, and saving lives with traffic data.<\/p>\n
As the variety of on- and off-board car data expands, so does the landscape of predictive maintenance solutions.<\/p>\n
Most widely used predictive car maintenance solutions based on vehicle safety data<\/h2>\n
Responding to the brisk demand for better control over car safety, companies offer numerous solutions that address the need for predictive car maintenance. Here are several to keep an eye on:<\/p>\n
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- Bosch<\/b><\/a> offers a predictive diagnostics solution providing vehicle-specific predictions based on smart data about a connected vehicle. Bosch\u2019s solution uses numerous sensors that ensure continuous monitoring of car components and systems. Thus, it collects data about power electronics, fuel filters, engine oil pumps, and beyond, providing 360-degree coverage of in-car components. With Bosch\u2019s predictive diagnostics solution, car owners and OEMs can prevent breakdowns and optimize maintenance planning.<\/li>\n
- Carmen<\/b><\/a> is one more solution that predicts vehicle issues and informs car owners about potential maintenance requirements before a malfunction occurs. Carmen collects data from in-vehicle sensors through a key dongle that plugs into a car. This dongle reads data while the customer is driving and sends it via Bluetooth to the driver\u2019s device. Then the data is uploaded to the cloud where it\u2019s processed and analyzed. When a problem occurs, the car owner and the repair shop both get an alert. This helps to ensure timely service and maintenance.<\/li>\n
- The TWAICE<\/b><\/a> startup provides software that assists OEMs in improving battery management and streamlining battery production. Predictive analytics integrated with AI provides feedback and monitors the system to avoid unnecessary damage and extend battery lifespans.<\/li>\n<\/ul>\n
How IT vendors can help to develop predictive car maintenance solutions<\/h2>\n
The connected car market is growing by leaps and bounds, primarily driven by safety features. To make sure predictive automotive maintenance software is tailored to bring greater safety, automakers need to consider many aspects.<\/p>\n
Specifically, automotive companies need to ensure the integration of vehicle safety data with other business data as well as precise vehicle data analytics and management for richer context and broader application. Plus, it\u2019s equally important to ensure vehicle data protection and scalability and improve the overall customer experience.<\/p>\n
This is where IT vendors like Intellias can help OEMs. By leveraging the experience of automotive software service providers, automakers and Tier 1 companies can leave the lion\u2019s share of programming tasks to their dedicated teams and focus on manufacturing, strategy, and business functions. Working in synergy, OEMs and software vendors can take predictive car maintenance to the next level of safety and win consumers\u2019 hearts and minds.<\/p>\n
\n - Carmen<\/b><\/a> is one more solution that predicts vehicle issues and informs car owners about potential maintenance requirements before a malfunction occurs. Carmen collects data from in-vehicle sensors through a key dongle that plugs into a car. This dongle reads data while the customer is driving and sends it via Bluetooth to the driver\u2019s device. Then the data is uploaded to the cloud where it\u2019s processed and analyzed. When a problem occurs, the car owner and the repair shop both get an alert. This helps to ensure timely service and maintenance.<\/li>\n
- Radar-based systems.<\/b> Radar (radio detection and ranging) sensors use radio waves to collect data. Radar sensors make a crucial contribution to the overall function of autonomous driving. They fulfill several functions: blind spot detection, lane-change assistance, collision warning and avoidance, parking and brake assistance, cross-traffic monitoring, and automatic distance control.<\/li>\n
- Types of vehicle data collected and managed by connected cars<\/a><\/li>\n