Showing posts with label Autonomous cars. Show all posts
Showing posts with label Autonomous cars. Show all posts

The Integration of Robotics in Autonomous Car Manufacturing

Autonomous car manufacturing has undergone a revolutionary transformation with the integration of robotics. Modern robotic systems streamline assembly lines, enhance precision, and reduce production time. Robotics not only improves efficiency but also ensures high-quality autonomous vehicles are produced consistently.

Robots in autonomous car factories handle tasks ranging from welding and painting to complex sensor installation. The use of artificial intelligence (AI) in robotics allows for adaptive operations, enabling machines to adjust in real-time to different assembly requirements. This combination of AI-driven robotics and automation accelerates production and minimizes errors.

Furthermore, robotic systems contribute to cost reduction and workplace safety. By automating repetitive and dangerous tasks, manufacturers can focus human expertise on innovation and quality control. The integration of robotics is therefore essential for the evolution of autonomous vehicle manufacturing and the future of smart mobility.

In conclusion, embracing robotics in autonomous car production is a key driver for efficiency, quality, and innovation. As technology advances, the synergy between humans and robots will shape the next generation of intelligent vehicles.

autonomous cars, robotics, AI, autonomous vehicle manufacturing, smart mobility, automation, robotics integration, car factory, automotive technology, production efficiency


How Automotive Engineers Test Autonomous Cars

Autonomous vehicles are revolutionizing the automotive industry, but ensuring their safety and reliability requires extensive testing. Automotive engineers use a variety of methods to evaluate self-driving cars before they hit the road.

Simulation Testing for Autonomous Vehicles

One of the first steps in testing autonomous cars is simulation. Engineers use virtual driving environments to replicate real-world scenarios such as heavy traffic, pedestrian crossings, and sudden obstacles. This allows developers to identify and fix software errors without risking human safety.

Closed-Track Testing

After simulations, autonomous cars are tested on closed tracks. These controlled environments let engineers evaluate the car's sensors, lidar and radar systems, and decision-making algorithms under realistic conditions. Engineers can safely recreate hazardous situations, such as unexpected lane changes or emergency braking scenarios.

Real-World Road Testing

Finally, autonomous cars undergo real-world testing with safety drivers onboard. Engineers monitor vehicle behavior in urban and highway conditions to ensure compliance with traffic laws and to assess performance in unpredictable situations like bad weather, construction zones, and erratic drivers.

Data Collection and Continuous Improvement

Every test generates valuable data. Engineers use this information to improve autonomous driving algorithms, optimize sensor performance, and enhance overall vehicle safety. Continuous testing and iteration are key to the successful deployment of autonomous vehicles.

By combining simulation, closed-track, and real-world testing, automotive engineers ensure that autonomous cars are not only innovative but also safe and reliable for future drivers.

autonomous cars, self-driving vehicles, automotive engineering, car testing, simulation testing, closed-track testing, real-world testing, lidar, radar, vehicle safety, AI in automotive


Quantum Sensors in Autonomous Cars: Revolutionizing Vehicle Technology

Quantum sensors are emerging as a game-changer in the field of autonomous vehicles. Unlike traditional sensors, quantum sensors leverage the principles of quantum mechanics to provide ultra-precise measurements of motion, position, and environmental conditions. This technology enhances the safety, efficiency, and reliability of self-driving cars.

How Quantum Sensors Work

Quantum sensors operate using phenomena such as superposition and entanglement, allowing them to detect minute changes in acceleration, rotation, and magnetic fields. In autonomous cars, this translates to more accurate navigation, even in challenging environments where GPS signals are weak or unavailable.

Benefits for Autonomous Vehicles

  • Enhanced Navigation: Quantum sensors improve position tracking, reducing the risk of accidents caused by GPS errors.
  • Safety: High-precision sensing allows vehicles to respond faster to obstacles and changing road conditions.
  • Energy Efficiency: Optimized driving routes and precise motion sensing lower energy consumption.

Future of Quantum Sensors in Cars

As quantum technology matures, we can expect widespread adoption in autonomous vehicles, making them safer and more reliable. Manufacturers are investing heavily in integrating quantum sensors into their next-generation self-driving systems.

In conclusion, quantum sensors represent the next frontier in automotive innovation, providing autonomous cars with unprecedented levels of precision and performance

Quantum Sensors, Autonomous Cars, Self-Driving Vehicles, Automotive Technology, Quantum Mechanics, Navigation Systems, Vehicle Safety, Smart Cars, Future Cars, High-Precision Sensors

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How Autonomous Cars Learn Through Simulation

Autonomous cars rely on advanced simulation technology to learn driving behaviors safely and efficiently. Using virtual environments, developers can train self-driving algorithms without risking real-world accidents.

Simulation allows autonomous vehicles to experience rare traffic scenarios and practice decision-making in complex situations. By integrating machine learning and AI algorithms, cars can continuously improve their navigation skills and object detection.

Modern simulators provide realistic 3D environments, dynamic traffic patterns, and sensor data replication. This enables developers to test autonomous driving software extensively before deploying it on public roads.

In addition, simulation accelerates the training process for self-driving cars, allowing them to handle emergency scenarios that are hard to reproduce in real life. Overall, learning through simulation is a critical step in autonomous vehicle development.

By leveraging virtual testing platforms, autonomous cars become safer, smarter, and ready for real-world challenges.

autonomous cars, self-driving, simulation, AI, machine learning, virtual testing, 3D environment, driving algorithms, autonomous vehicle development, object detection


Autonomous Car Testing: How Engineers Validate Safety and Performance

Autonomous vehicles are revolutionizing the future of transportation. To ensure these self-driving cars operate safely and efficiently, engineers conduct rigorous autonomous car testing in both simulated and real-world environments.

Simulation Testing

Before hitting the roads, engineers utilize advanced driving simulations to evaluate vehicle behavior. Simulation platforms allow testing of collision avoidance, lane keeping, and adaptive cruise control under various traffic conditions. These controlled environments reduce risk and accelerate development.

Closed-Track Testing

After simulations, vehicles are tested on closed tracks that replicate urban and highway conditions. Here, engineers validate sensor performance, vehicle response, and autonomous decision-making in a safe and controlled setting. This step ensures the car can handle complex scenarios before public deployment.

On-Road Testing

Finally, autonomous cars undergo on-road testing with safety drivers present. Real-world testing evaluates system reliability and edge case handling in dynamic traffic environments. Data collected from these drives is critical for refining software and enhancing overall vehicle safety.

Data-Driven Safety Validation

Engineers leverage big data analytics and machine learning to identify potential safety issues. Continuous performance validation ensures autonomous systems meet strict safety standards and comply with regulations, building trust in self-driving technology.

In conclusion, autonomous car testing involves a systematic approach from simulations to real-world evaluation. These rigorous processes are essential for ensuring both safety and high performance of self-driving vehicles.

Autonomous Cars, Self-Driving Cars, Car Safety, Vehicle Performance, Driving Simulation, Sensor Technology, AI in Cars, Autonomous Vehicle Testing, On-Road Testing, Closed-Track Testing


The Future of Mobility: How Modern Automotive Technology Is Transforming Transportation

In today's rapidly evolving world, modern automotive technology is reshaping how we move. From electric vehicles and autonomous cars to smart transportation systems, the future of mobility is becoming increasingly connected, efficient, and sustainable.

Electric Vehicles: Driving Toward a Greener Future

Electric vehicles (EVs) are at the forefront of reducing carbon emissions. Advanced battery technology, faster charging infrastructure, and longer driving ranges make EVs a practical choice for consumers worldwide.

Autonomous Vehicles: Redefining Transportation Safety

Self-driving cars use artificial intelligence, sensors, and machine learning to navigate roads safely. By minimizing human error, autonomous vehicles promise a future with fewer accidents and smoother traffic flow.

Connected and Smart Transportation Systems

Connected mobility integrates vehicles with traffic management systems and smart city infrastructure. This smart transportation technology enhances real-time traffic monitoring, predictive maintenance, and optimized routing, improving overall efficiency.

Sustainability and Urban Mobility

The future of mobility focuses on sustainable urban transportation. Shared mobility, e-bikes, and smart public transit solutions reduce congestion and lower environmental impact, creating cleaner and more livable cities.

Conclusion

As modern automotive innovations continue to advance, our transportation landscape will become safer, smarter, and greener. Staying informed about emerging mobility technologies is essential for individuals, businesses, and policymakers alike.

future of mobility, modern automotive technology, electric vehicles, autonomous cars, smart transportation, connected mobility, sustainable transportation, automotive innovation, urban mobility, EV technology


The Role of LIDAR in Autonomous Cars

LIDAR technology has become one of the most essential components in the development of autonomous cars. By using laser-based distance measurement, LIDAR creates precise 3D maps of the environment, helping self-driving vehicles interpret surroundings with exceptional accuracy.

What Is LIDAR Technology?

LIDAR (Light Detection and Ranging) is a sensing method that sends laser pulses to measure the distance between the vehicle and surrounding objects. This system builds real-time 3D mapping, enabling autonomous cars to understand road conditions, traffic elements, and unexpected obstacles.

Why LIDAR Is Important for Self-Driving Vehicles

  • High-Precision 3D Mapping: LIDAR sensors generate detailed environmental models, improving driving accuracy.
  • Excellent Object Detection: LIDAR identifies cars, pedestrians, barriers, and road edges even in low light.
  • Improved Safety: The system reduces collision risks, enhancing the reliability of autonomous driving technologies.
  • Real-Time Decision Making: LIDAR provides fast data processing to support advanced automotive AI.

How LIDAR Works in Autonomous Cars

The LIDAR module rotates and continuously emits laser beams. These beams bounce back from objects, allowing the sensor to calculate their distance. The car’s onboard computer combines this information with other systems such as RADAR, cameras, and ultrasonic sensors to create a complete 360-degree environmental model.

The Future of LIDAR in Automotive Technology

As automotive innovation advances, LIDAR technology continues to become more compact, affordable, and powerful. Many leading manufacturers integrate LIDAR into next-generation autonomous cars to improve navigation accuracy, route planning, and overall safety. Whether for urban mobility or long-distance travel, LIDAR will remain a critical technology in the autonomous driving revolution.

LIDAR, autonomous cars, self-driving vehicles, automotive technology, 3D mapping, sensor systems

Top Autonomous Car Projects Worldwide

Autonomous driving technology has rapidly evolved over the past decade, driven by major automotive companies and advanced research organizations. Today, the world is witnessing a new era of innovation where self-driving cars, AI-powered mobility, and smart transportation systems shape the future of mobility. This article highlights the most influential and advanced autonomous car projects worldwide that are redefining global transportation.

1. Waymo Autonomous Driving Project (USA)

Waymo, a leader in the autonomous vehicle industry, continues to push boundaries with its fully driverless robotaxi services operating in multiple U.S. cities. The project integrates LiDAR, radar, HD mapping, and machine learning to ensure safe navigation. Waymo's progress remains a key driver behind global autonomous vehicle development.

2. Tesla Full Self-Driving Program (USA)

Tesla's Full Self-Driving (FSD) project focuses heavily on real-world data and neural networks. With millions of vehicles collecting daily driving information, Tesla builds one of the largest autonomous driving datasets worldwide. The company aims to achieve full autonomy through continuous software updates and advanced AI modeling.

3. Baidu Apollo Autonomous Platform (China)

Baidu Apollo is one of the biggest open platforms for autonomous driving technology. With partnerships involving over 200 global companies, Apollo supports robotaxi services, autonomous buses, and commercial logistics. The project plays a critical role in accelerating China’s smart mobility ecosystem.

4. Cruise Autonomous Vehicle Program (USA)

Backed by General Motors, Cruise focuses on electric autonomous vehicles designed for urban environments. Cruise’s robotaxi fleet has been tested in complex traffic scenarios, contributing significantly to the advancement of self-driving mobility solutions in major U.S. cities.

5. Mobileye Autonomous Driving Systems (Israel)

Mobileye, an Intel company, specializes in computer vision and advanced driver-assistance systems (ADAS). Its EyeQ chips power millions of vehicles globally. Mobileye's autonomous project utilizes high-definition mapping and AI to develop scalable self-driving solutions for automakers worldwide.

Conclusion

The growth of autonomous car projects worldwide reflects a transformative shift in transportation. As AI, sensors, and robotics continue advancing, autonomous mobility will become safer, more efficient, and widely accessible. These leading projects demonstrate the future potential of global smart transportation.

Autonomous Cars,Self Driving Technology,AI Mobility,Smart Transportation,Autonomous Vehicles


The Role of Cloud Computing in Cars

Cloud computing is transforming the automotive industry by providing advanced data management, real-time analytics, and seamless connectivity. Modern vehicles now rely on cloud-based platforms to enable smart navigation, predictive maintenance, and enhanced infotainment systems.

Enhancing Vehicle Connectivity

Cloud computing allows cars to connect to remote servers, sharing critical information about traffic, road conditions, and vehicle performance. This connectivity improves safety, efficiency, and the overall driving experience.

Data Management and Analytics

With cloud solutions, automotive manufacturers can collect and analyze vast amounts of data from vehicles. This enables predictive maintenance, reduces downtime, and enhances operational efficiency.

Supporting Autonomous Driving

Cloud computing provides the computational power and real-time data access required for autonomous vehicles. By leveraging cloud AI services, cars can make smarter decisions on the road.

Future of Automotive Cloud Solutions

The integration of cloud computing in cars continues to evolve, enabling innovations like vehicle-to-everything (V2X) communication, over-the-air updates, and personalized driving experiences. Embracing cloud technologies will be essential for the next generation of smart vehicles.

Cloud computing, automotive cloud, connected cars, smart vehicles, autonomous vehicles, predictive maintenance, vehicle analytics, V2X communication, real-time data, infotainment systems.

The Role of AI in Modern Vehicles

Artificial Intelligence (AI) is transforming the automotive industry at an unprecedented pace. Modern vehicles are now equipped with AI-powered systems that enhance safety, improve driving efficiency, and offer personalized experiences for drivers.

AI in Driver Assistance Systems

Advanced Driver Assistance Systems (ADAS) use AI algorithms to monitor the surroundings of a vehicle, detect obstacles, and assist with tasks like lane keeping, adaptive cruise control, and automated parking. These AI systems help reduce accidents and increase overall road safety.

AI in Autonomous Vehicles

Self-driving cars rely heavily on AI technologies such as computer vision, machine learning, and sensor fusion. AI enables vehicles to understand traffic patterns, recognize objects, make real-time decisions, and navigate safely without human intervention.

AI for Predictive Maintenance

AI analyzes data from vehicle sensors to predict potential failures before they occur. Predictive maintenance helps car owners avoid unexpected breakdowns, reduces repair costs, and extends the lifespan of the vehicle.

AI and Connected Vehicles

Connected vehicles use AI to communicate with each other and with traffic infrastructure. This technology enhances traffic flow, reduces congestion, and supports smart city initiatives, creating a safer and more efficient driving environment.

The Future of AI in Vehicles

As AI continues to evolve, vehicles will become even more intelligent, offering enhanced safety features, improved energy efficiency, and fully autonomous driving capabilities. The integration of AI in modern vehicles marks a new era in automotive technology.

By understanding the role of AI in modern vehicles, drivers and automotive enthusiasts can appreciate the cutting-edge technology shaping the future of transportation.

Top 10 Automotive Technologies Revolutionizing the Industry


The automotive industry is rapidly evolving, driven by cutting-edge technologies that enhance safety, efficiency, and driving experience. In this article, we explore the top 10 automotive technologies that are transforming vehicles and reshaping the future of mobility.

1. Autonomous Driving Systems

Self-driving cars are no longer a distant dream. Autonomous driving technologies use AI, sensors, and real-time data to navigate roads safely, reducing human error and improving traffic efficiency.

2. Electric Vehicles (EVs)

The shift towards electric vehicles is accelerating due to environmental concerns and advancements in battery technology. EVs offer zero emissions and lower operating costs compared to traditional vehicles.

3. Advanced Driver Assistance Systems (ADAS)

ADAS includes features like lane departure warning, adaptive cruise control, and automatic emergency braking. These systems enhance vehicle safety and assist drivers in challenging road conditions.

4. Connected Vehicles

Connected car technologies integrate internet connectivity and vehicle-to-everything (V2X) communication. This enables real-time traffic updates, remote diagnostics, and enhanced infotainment experiences.

5. Smart Infotainment Systems

Modern vehicles feature smart infotainment systems with voice recognition, AI assistants, and seamless smartphone integration, enhancing driver convenience and in-car entertainment.

6. Lightweight Materials

Automakers are increasingly using lightweight materials like carbon fiber and aluminum to reduce vehicle weight. This improves fuel efficiency and overall vehicle performance.

7. Advanced Battery Technology

High-capacity lithium-ion and solid-state batteries power the next generation of EVs, offering longer ranges, faster charging, and improved safety.

8. Vehicle-to-Grid (V2G) Technology

V2G technology allows electric vehicles to feed energy back into the grid, helping stabilize electricity demand and supporting renewable energy integration.

9. Artificial Intelligence in Vehicle Design

AI-powered design tools optimize vehicle aerodynamics, material selection, and component durability, speeding up innovation in the automotive industry.

10. 3D Printing and Additive Manufacturing

3D printing enables rapid prototyping and custom parts production, reducing manufacturing costs and allowing more flexible vehicle design solutions.

These technologies collectively contribute to safer, smarter, and more sustainable mobility. The automotive industry is poised for a future where innovation drives performance, efficiency, and connectivity.

How Autonomous Cars Are Changing Urban Mobility


Autonomous cars, also known as self-driving vehicles, are revolutionizing the way people move in urban environments. By integrating advanced sensors, AI, and machine learning, these vehicles can navigate city streets with minimal human intervention.

Impact on Traffic Flow

One of the key benefits of autonomous cars is their ability to optimize traffic flow. Through real-time communication and predictive algorithms, self-driving cars reduce congestion, improve travel times, and decrease the likelihood of accidents.

Environmental Benefits

Autonomous cars contribute to urban sustainability by promoting energy-efficient driving patterns. Reduced traffic jams and smoother acceleration/deceleration cycles lead to lower carbon emissions, helping cities meet environmental goals.

Enhanced Accessibility

Self-driving vehicles increase mobility for individuals who cannot drive, including the elderly and people with disabilities. This technology creates a more inclusive urban transport system, allowing more people to access jobs, healthcare, and social activities.

Challenges and Considerations

Despite the benefits, autonomous cars face challenges such as regulatory frameworks, cybersecurity risks, and public acceptance. Urban planners and policymakers must address these issues to ensure safe and efficient integration into city streets.

The Future of Urban Mobility

As autonomous car technology continues to evolve, cities will need to rethink infrastructure, parking, and public transport integration. The combination of self-driving cars, smart traffic management, and sustainable urban planning promises a transformative future for urban mobility.

Keywords: autonomous cars, self-driving vehicles, urban mobility, traffic optimization, sustainable transport, AI driving technology

AI Behind the Wheel: How Machine Learning Makes Cars Learn and Drive

 What is AI in the context of autonomous vehicles?

In autonomous vehicles, AI is an intelligent software system responsible for perception, decision-making, and control on behalf of humans. The primary goal is safe, accurate, and fast driving in real time.

Machine Learning (ML) and Deep Learning (DL) are key.

The heart of AI in self-driving cars is the use of machine learning (ML) techniques, particularly deep learning (DL), which uses complex, multi-layered neural networks to learn from massive amounts of data.

1. Perception and Understanding of the Environment

This is the stage where AI must learn to "see" the world like humans, but faster and more accurately:

  • Input: The system receives raw data from surrounding sensors (cameras, LiDAR, radar).

  • Deep Learning Training: Developers use Deep Neural Networks to train the AI ​​to recognize and classify objects in the data:

  • Objects: Other cars, pedestrians, bicycles, trees, animals

  • Traffic Signs and Signals: Learn the shape and color of stop signs, speed limits, and traffic light status.

  • Lane Lines: Accurately identify and follow lane lines. Even in blurry road conditions,


2. Prediction

Autonomous vehicles need to know not only where objects are, but also predict what those objects will do next. ML plays a key role in predicting probabilities:

  • Motion Analysis: AI analyzes the movement patterns of the vehicle ahead (e.g., whether it's braking or changing lanes) and pedestrians (e.g., whether it's about to step out of the road).

  • Learning from Historical Data: Machine learning learns from millions of miles of real-world driving data to build models of various road behaviors, enabling the vehicle to prepare for unexpected situations.


3. Decision Making

After the system understands and predicts, the AI ​​decides what to do next in real time.

  • Path Planning: The system calculates the optimal path within the lane and avoids objects.

  • Control Command: Commands are translated into robotic control signals, such as:

  • Steering Command: Turn slightly to maintain distance from the curb or make an emergency evasive maneuver.

  • Acceleration/Brake Command: Accelerate, decelerate, or apply emergency braking (AEB).


Continuous Learning Cycle

The difference between autonomous cars and conventional cars is that the AI ​​can constantly learn and improve itself (Learn from Data).

  • Data Collection: When autonomous cars are on the road, they collect data on various driving situations (e.g., weather, traffic congestion, and human driving).

  • Over-the-Air (OTA) Software Updates: This data is sent back to the development center to train the AI ​​model to be more intelligent. When new software updates are released, every car is updated via the internet, allowing every car to learn from the experience of other cars on the road around the world.

Machine learning and deep learning are what transform cars from "command-following machines" into "thinking and decision-making vehicles," which is the core behind today's autonomous driving capabilities.


Key Technologies:

  • AI, Machine Learning, Deep Learning, Artificial Intelligence


System Operations:

  • Automotive Software, Data Processing, Autonomous Driving


Broad Topics:

  • Autonomous Cars, Automotive Technology, Neural Networks


AI, Machine Learning, Deep Learning, Autonomous Cars, Automotive Software, Autonomous Driving


This image is: AI is the brain of the car (Concept). This image shows an autonomous car with the AI ​​brain or processing chip in the center of the car and lines connecting to various parts such as sensors (cameras, LiDAR, radar) and control systems (steering, brakes) to convey that AI is the decision-making center.



BlackBerry QNX Partners with Obigo to Build a Better Browser for Your Car

John Wall
Senior Vice-President and Head of QNX Software Systems
 
  

bentley-2

Software plays a big role in current vehicles. The best example is your in-vehicle infotainment system. With just a few taps, you can play music from your mobile device, view and manage advanced navigation systems, make phone calls, tap into traffic reports and weather forecasts, all from your car’s center display console. Drivers and passengers love these systems and are dependent on them. However, Infotainment has its own challenges to address. Security aside, the infotainment system needs to be able to provide support for the latest and greatest web browser technology to give the users access to the content they want the instant they want it, whether on the web or off. You do not, after all, want to be fumbling with an unresponsive display when you are on the road.

At BlackBerry, we are constantly seeking ways to improve the vehicle cockpit experience and to enhance our QNX CAR Platform for Infotainment (QNX CAR). This is why we are proud to announce our partnership with Obigo a leading Korea-based provider of mobile Internet services and browser software. Obigo is working with is to deliver a powerful, Chrome Blink-based HTML5 engine, which will enhance the browser experience of your infotainment systems.
QNX_2015_concept_car_Maserati_incoming_call-2

As part of the agreement, our internal HTML5 team will be augmented by Obigo’s team of experts who will help to optimize the latest Blink browser technology with QNX CAR (pictured). QNX CAR, our best in class Infotainment system, has more than 50% global market share and is present in over 60 million vehicles. With QNX bringing this new browser to market, vendors and manufacturers can enrich their driving experience through new applications and services, and drive further customer satisfaction.

“Many automotive OEMs and Tier-1s still view open source HTML browsers as lightweight,” explains Obigo CEO David Hwang. “With almost two decades of browser experience on embedded systems, we have been working to change those perceptions with highly optimized technology that address open source browser performance issues. Working with BlackBerry-QNX, we plan to develop a product that will boost interest in HTML5 technology for emerging in-vehicle applications and services.”

Obigo’s technology will also simplify the coding of HTML 5 browsers for new infotainment systems and accelerate the product development cycle. Obigo joins the ecosystem of BlackBerry QNX partners that collectively offer the best-in-class system level solution for our infotainment customers.

From Concept to Reality: BlackBerry-QNX's Groundbreaking CES Tradition

Thomas Bloor
Business Development Manager, QNX BlackBerry

The annual Consumer Electronics Show in Las Vegas has been growing in importance for the automotive industry over the years. You can hardly fail to notice that this year, as in previous years, the big automakers vie for floor space and attention with the glut of big screen TVs and other consumer goods. As always, BlacBerry QNX will be in the North Hall, proudly in the middle of the big automotive OEMs. 

At CES BlackBerryQNX has an enviable history of bringing concept cars that rival anything on the show floor, with one important difference – ours are not pure flights of fancy, and we show technologies that will become realities in the near future.

We started this trend back in 2010 with an LTE-connected Toyota Prius – 18 months before the first commercial LTE deployment in mid-2011. Working with Alcatel-Lucent to provide the experimental network, we demonstrated Google maps functionality with local search and an embedded Pandora radio app in a car for the first time. Connectivity is standard in many cars today, but in 2010 we demonstrated the future.
2012 brought us a CNET "Best of CES" award for demonstrating cloud-based natural language voice recognition, text-to-speech, and NFC based one-touch Bluetooth pairing.  Simply touching your phone to an NFC reader in the center console automatically paired the phone and car. 
In 2013 we got ahead of the trend for ever larger center stack displays – with detailed 3D maps and voice recognition Keyword Spotting – common today in smartphones but a first in a car. Simply saying "Hello Bentley" enabled you to start interacting with the natural language cloud based voice recognition Powered by AT&T’s Watson. 
2014 took literally us in a different direction. A 21-inch horizontally orientated center stack display extends across the dash, naturally extending the interaction and functionality towards the passenger.  Behind the screens the instrument cluster was integrated with the center stack running both driver information and IVI functions. With seamless controllability across the touch screen, physical buttons, and the jog wheel controls multi-modal input was highlighted across all available functionality. 

Not content with that, we foreshadowed greater integration of ADAS functionality warnings to the driver. In 2014 we warned the driver if local speed limits were exceeded through both the cluster and verbally through text-to-speech, and we followed this up in 2015 with a system that recommends an appropriate speed for upcoming curves based upon driving conditions and the radius of the bend.

So, what innovations will we be showing in 2017? I’m not allowed to tell you just yet but, in a first (for us), we’ll be showing both future and current production technologies and innovations.

Building on our products ranging from in-car acoustics through our comprehensive QNX-CAR application platform and to next generation driver assistance/autonomous drive we will be demonstrating how technology can enhance the user experience and increase safety for drivers and passengers.

While demonstrating technologies that will come to future production vehicles, these cars are not just "show floor wonders" because our automotive knowledge enables us to build demonstrators for the real world, which can be driven, thus allowing technologies to be experienced first-hand.






Holistic Security for the Software-Defined Car

Bill Boldt
Sr. Business Development Manager, Security
Blackberry Certicom



Due to high profile hacks on cars, it is hard to argue that without security you can have safety.   So, security is emerging as perhaps the most important factor in the evolution of the connected autonomous car.
 
Cars are the most software intensive systems in the universe with far more lines of code than even a state of the art jet fighter. By being such complex digital systems they have become prime targets for attack, and that is where cryptographic countermeasures come in.
Connecting the dots – in the emerging software-defined world safety increasingly
comes from security and security comes from cryptography. Robust cryptographic security implementation is how you increase trust, and when it comes to a car every system must be
trusted: inside the car, in the smart infrastructure, in emerging applications-based ecosystems, and in the manufacturing supply chain. When considering automotive security,
many factors come into play. Some are noted here:
                       
  • Automotive security fundamentally depends on the security of the operating system. For example, a microkernel architecture that separates critical OS components into their own protected memory partitions, provides temporal separation, and provides network security, among other things can greatly reduce the attack surface.
  • Security assets (crypto keys, serial numbers, etc.) must be securely installed into electronic devices such as Electronic Control Units (ECUs), domain/area controllers, and other processors. This process is called "personalization".
  • Electronic devices will often get personalized and installed into vehicles in globally located factories, which should utilize secure equipment and processes to ensure security of the devices.
  •  Devices must be updateable at dealers and repair shops. 
  • Aftermarket suppliers must be able to sell and update secure devices, and
  • OEMs must be able to authorize or not authorize specific electronic devices at
    manufacturing time and after the car is in use (for example to enforce warrantee policies).
And, there are many more.


Personalizing a device such as a networked ECU means that it will become one of a kind. However, by definition that device cannot be used anywhere else. It becomes a unique stock keeping unit (SKU), which is averse to the purpose of flexible, just in time manufacturing flows. Security versus manufacturing flexibility is a serious trade off that will play a part of any automotive security design decision.

Security robustness versus cost is another critical trade off, and applies to the manufacturing infrastructure and the design of the secure systems inside and outside the vehicle. Because security must be injected in the factory and in the field, a secure manufacturing system must have global reach, be manageable on a distributed basis, be updatable by various entities, and remain secure for years. In addition, security updates will increasingly be made over the air, and the systems that do that must by highly secure while being easy to manage. To maintain the maximum amount of flexibility, personalization and updating should be moved as close as possible to the very last minute, which is becoming a critical objective of the global manufacturing blue print. 

Blackberry Brings It All Together




In the car, outside the car, and in the manufacturing supply chain, security must be designed with best practices in mind right from the start, and BlackBerry Professional Services can help with that. BlackBerry QNX provides mission-critical automotive software proven in the automotive market.  QNX software is well known for safety and new products are setting the new standard for security.

BlackBerry's Certicom subsidiary provides certified cryptographic code and design consulting, as well as secure equipment and managed services that harden the automotive supply chain. Completing the picture, BlackBerry's secure OTA managed services make it easy to update software and security assets over the air. When it comes to automotive security, BlackBerry brings it all together.


On 64-bit and roadmap alignment

By Romain Saha
Strategic Alliances Manager
Blackberry QNX
 

One of the coolest things about my job is getting to see all the silicon roadmaps. OK, I’m a nerd. That is not a surprise to anyone I’m sure. Still, there’s a lot of amazing innovation going on. You just haven’t heard about. Yet. And you won’t hear it from me. Sorry.

Except maybe that the embedded world – at least in the areas we play – is going 64-bit. Pretty much already gone 64-bit actually. Intel architecture has been 64-bit for as long as I can remember. ARMv8 is almost exclusively 64-bit. It’s here. It’s real. You can buy it. Pretty much everybody has it.

Graphics is another area that is moving fast. The latest embedded GPUs are really impressive. I know of one chip that actually has two full-blown GPUs on a single die. The things I’m seeing on the bench are amazing. Light-years ahead of where we were only a generation ago.

Roadmap alignment is key for us. We need to make sure our products sing with our silicon partner’s technology. We need to make sure customers can take the latest SoCs and build the things stuff the world wants.

One of the things the world wants these days is a digital cockpit - a unified experience across multiple displays in the cabin.  That is happening today but it takes two SoCs to do it, one for cluster and one for infotainment system. It also takes space, power, cabling, connectors and inter-processor communication. Hassle. Lots of hassle I bet.

The obvious dream is to eliminate all this cost and complexity and just use a single SoC for both. Easy. Except digital instrument clusters and infotainment systems are different. Very different.

Infotainment systems need lots of horsepower. They use lots of memory. Navigation systems alone can drive addressing past 4 Gigabytes. Huge state diagrams. They also need lots of eye-candy. GPU performance is key. Complicated indeed.

Digital instrument clusters need incredibly smooth graphics performance but are relatively simple otherwise. Except that they are safety critical. Enter ISO26262 certification. Overlay that with making sure what you think you are rendering is actually what gets displayed. If the screen says P(ark) and the car is in R(everse) people get hurt. Or worse. Clusters are complicated too.


What do you need to make all this work? For sure you need a 64-bit safety-certified embedded OS. You need a hypervisor with the ability share graphics across virtual machines. You need ISO26262 top to bottom as well as a way to ensure cluster rendered output matches the intended output. And you need an SoC with the juice to make it all happen. That’s a lot. No wonder people think the single chip digital cockpit is still a dream.





Autonomous Cars Part 1-- And Now for Something Completely Different: The Autonomous Accident



Kaivan Karimi
SVP of Strategy and Business Development
BlackBerry Technology Solutions (BTS)

A few weeks ago a self-driving Tesla Model S in Autopilot mode crashed into a large semi-trailer in Williston Florida.   This is pretty much what lawyers call a case of first impression, and rightfully so.  This unprecedented event brought up a bunch of questions, and it is clear that we are now on the cusp of the autonomous (i.e. robot-driven) automotive future.  With that comes a completely different mix of risks, liabilities, safety concerns, responsibilities, ownership models, insurance platforms, and regulatory oversight. 

Car crashes are, and should be, a big deal. They are the number one reason for death among young people and number five overall, claiming over 32,000 American lives each year. Some news outlets have questioned the sanity of allowing driverless cars on the road all together. Fairly or unfairly, the whole notion of driverless cars is experiencing knee-jerk reactions. It is easy to see why the first known death caused by a self-driving car in the history has focused everyone’s attention on autonomous vehicles.

This incident is much like how Bridget Driscoll made the history in 1896 by being the first pedestrian being struck and killed by a gas-powered car (at a top speed of four miles per hour).   Thanks to the sensationalism of the press, the Florida crash got much more coverage in the news cycles than the more positive story about the Missouri man who used his Tesla Model X in autopilot mode to get to the hospital when he suffered from a debilitating blood clot on the highway.  Tesla Autopilot saved his life, and that is real (and good) news.  Nothing like that has happened before—a robot saving a man’s life.  Amazing.

These issues have made so much headlines that it made it to the white house, and president Obama wrote an op-edmostly in support of the technology.  President Obama wrote that safer, more accessible driving, and less congested, less polluted roads are what harnessing technology for good can look like referring to self-driving car technologies. He also said that we have to get it right. Americans deserve to know they’ll be safe today even as we develop and deploy the technologies of tomorrow.
The accident has given rise to discussions about what types of sensors should have been in place to avoid that accident.  Also, as you would expect, there is a lot of questioning by legislators about the need for such technology, and how it can be regulated. A proper outcome of the crash has been awareness that autonomous driving is a public safety issue.  This is multi-faceted and includes technology (i.e. hardware, software, and architecture), economics, policy implementation, liability, and oversight factors.

I started following the development of autonomous vehicles when I first heard about Google’s so-called “self-driving car” project back in 2009. While I knew about DARPA’s initiative around this idea in mid 2000s, a commercial entity like Google picking up the project lends real credibility. Back in the 2010-2011 timeframe, my team and I were working on Freescale’s MCU strategies, and through that I got to understand the role of Active Driver Assistant System (ADAS) and the numerous architectural considerations and technologies needed to make autonomous driving a reality. 

Now at BlackBerry, I am working with our QNX software team on ADAS development.The QNX perspective, of course, comes from the software side with expertise in instrument clusters, functional safety, hypervisor infotainment, and telematics. When you add that to  Certicom’s cryptographic security expertise,  and BlackBerry’s Over-the-Air (OTA),  updates for automotive security life cycle management, you have what you need for safety and security of the software-defined autonomous future. The evolution to connected autonomous vehicles is transitioning through different stages that in fact were defined by the U.S. Department of Transportation's National Highway Traffic Safety Administration. SAE has defined levels as well.  

                                                          
                                                           Source: NHTSA

Most car OEMs that we are working with have autonomous driving pilot programs in place. That is no surprise.  Even before the Tesla Autopilot accident, it was hard to open a technology magazine or website and not see a mention of self-driving cars and various pilot programs around the world. Cars are becoming cool again due to new technological evolution.   This is similar to how cellphones became cool in the early 2000s when the emergence of 3G made the notion of smartphones real.   Cars are much more than a phone, obviously, and the sky is the limit.  Software, semiconductor, networking, cryptography, sensors, communications, electric/hybrid engine, charging, display, augmented reality, smart highways, retail, and other technologies all converge on the car platform.  These things are quickly redefining the car, the highway, ownership models, insurance, and society itself. 
 
Some of the items to consider are the forms that vehicles will adopt due to automation, such as autonomous cars,  to  self-driving busses  , self-driving trucks,  and DARPA’s 132-foot long Sea Hunter unmanned Submarine-Hunter Drone .

Hardware + Software

Self-driving vehicles, or self-propelled anything, are based on an intimate relationship between electronics hardware and software to create not only a perceiving, processing, and actuating system, but a system that is safe, secure, and reliable.   While that last part seems obvious, it is not all that easy to accomplish.   Safety, security, and reliability come only from careful design based upon experience—experience that can make hardware and software work seamlessly.
 
Starting with the hardware, if you look at automotive microprocessors and microcontrollers, you can see that their complexity has skyrocketed to meet real time requirements of active safety elements such as vision processing, sensor fusion, and control algorithms, while still maintaining stringent power budgets.




Advanced driver assistance systems (ADAS) are the backbone of autonomous vehicles, obviously, and that it is based uponmultiple application cores and hardware accelerators.   ADAS, software platforms must provide high performance by combining symmetric multiprocessing on application cores with support for built in accelerators such as vision processing engines or graphics processing units (GPUs).   Examples of applications range from four camera surround view systems, to a single camera forward facing collision avoidance system, to a sensor fusion hub.
Of course, the most important aspect of anything automotive is safety.  The old adage of safety first is still valid, and getting even more so as robotic cars start to drive themselves.  Therefore, there has to be real safety know-how at the core of the design and implementation of ADAS.  This is where safety standards compliance comes in.  The QNX Platform for ADAS is a great example of safety-centered software for the autonomous car.   The platform is certified by TÜV Rheinland to ISO 26262 ASIL-D.

More details will be addressed in a future blog, but are presented here to illustrate that software must be compliant with safety standards if it is to be taken seriously.  How safety is achieved by a software architecture is by ensuring that system faults in one area do not affect other areas.   This is accomplished by using a microkernel architecture the operating system (OS) to create isolation of failed components, and allowing them to be restarted dynamically while the rest of the system continues to operate. This type of adaptive partitioning technology safeguards the operation of the safety-critical components by ensuring they are never starved of CPU cycles. With a microkernal approach, traditional OS services can be contained in separate, hardware-protected address spaces in the same manner as applications.

The next blog will focus on the individual subsystems used in an ADAS platform in the connected autonomous car.    In addition, other connected autonomous car technologies will be covered in subsequent blogs, including security, Domain/Area-controller evolution, more about safety, and other technologies needed, plus use-case and financial considerations related to autonomous cars.   The story of the software-defined automotive future is just starting to be written.   For more see the QNX web site.

 


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Kaivan Karimi is the SVP of Strategy and Business Development at BlackBerry Technology Solutions (BTS). His responsibilities include operationalizing growth strategies, product marketing and business development, eco-system enablement, and execution of business priorities. He has been an IoT evangelist since 2010, bringing more than two decades of experience working in cellular, connectivity, networking, sensors, and microcontroller semiconductor markets. Kaivan holds graduate degrees in engineering (MSEE) and business (MBA). Prior to joining BlackBerry, he was the VP and General Manager of Atmel wireless MCUs and IOT business unit.


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