From advanced sensors to artificial intelligence, vehicles of all types are quickly becoming home to the latest electronics technology.
The transportation space has seen a burst of technology—not in one particular area, but rather across the board from improvements in electrical power systems to extremely sophisticated telematics to self-driving cars. Cars today have more electronics that ever before. Much more is coming, though, as features such as advanced driver-assistance systems (ADAS) become standard features instead of expensive options.
These changes are being made possible by improvements in sensors, processors and memory, software, and even human interfaces that need to be integrated in real time (Fig. 1). Here are some of the latest technologies and how their relationship with other technologies makes them even more important in automotive environments.
Smartphones have turned tiny digital cameras into commodity items in a way that other applications—digital cameras, for instance—could not. Automotive applications continue to benefit from the availability of cameras that can stream 4K video. High-definition cameras are being used for obstacle and object recognition for forward-looking ADAS applications in conjunction with artificial-intelligence (AI) machine-learning (ML) software. Here the higher resolution is important, and it’s useful for backup cameras, too.
Multiple cameras are also being used to provide a birds-eye view around the car. Renesas’R-Car development kit knits together video streams from four cameras into a 360-degree view (Fig. 2). This is very useful when parking or navigating in tight quarters. More advanced ADAS systems highlight areas of potential oncoming collisions.
Two other range sensors that have shown significant improvements lately are LiDAR and phased-array radar. The general technology is not new, but major advances in miniaturization and cost reductions will affect when and where these systems are being utilized.
For example, Innoviz (Fig. 3), LeddarTech, Quanergy, and Velodyne are just a few companies delivering 3D, solid-state LiDAR systems. These systems, which are applicable in other areas like robotics (see “Bumping into Cobots “), are getting so small that multiple units will be hidden around a car.
Phased-array radar overcomes many of the limitations of LiDAR, allowing it to operate in rain and snow that can otherwise fool optical systems. Radar can be used to complement LiDAR and image systems. A number of companies are working to deliver technology in this area. For example, Texas Instruments’ (TI) single-chip millimeter-wave sensor, mmWave, handles 76- to 81-GHz sensor arrays for sensor and ADAS applications (see “Low-Cost Single Chip Drives Radar Array”).
All of these technologies have applications in other areas from manufacturing to security and even 3D scanning and printing.
AI and ML are garnering the limelight these days because they bring efficient image recognition to ADAS that’s critical for safe self-driving or augmented driving experiences. The underlying technology is based on deep neural networks (DNNs) and convolutional neural networks (CNNs) (see “What’s the Difference Between Machine Learning Techniques?”).
Neural networks will not replace conventional software applications, even in automotive environments, but they solve hard problems. Combined with new hardware, they can also do it in real time, which is needed in safety-critical applications such as self-driving cars. Multicore processors help in this case, but GPUs work even better (Fig. 4). Custom hardware bests them (see “CPUs, GPUs, and Now AI Chips”) all, and even specialized digital-signal processors (DSPs) can handle machine-learning chores (see “DSP Takes on Deep Neural Networks”).
The parallel-processing nature of these solutions plays well to the multicore and transistor count growth in designs, even as upper-level clock frequencies have peaked. The more tailored solutions also have lower power requirements compared to more conventional processor solutions.
The in-vehicle infotainment (IVI) system advance is changing what drivers and passengers are able to visualize, as well as how they can link their smart devices and cloud-based applications to their car. Cellular-based Wi-Fi hot spots in a car are available from all vehicle manufacturers. The plethora of options requires a more robust and open approach. On that front, the GENIVI Alliance (see “Automotive Technology Platform Developed for Linux-Based Systems”) fosters open standards that are operating-system agnostic.
The The Linux Foundation’s Automotive Grade Linux (AGL) is one example of an IVI system that has received wide vendor support. AGL will be used in Toyota’s 2018 Camry (Fig. 5) as well as future Toyota vehicles (see “Toyota Including Automotive Grade Linux Platform in 2018 Camry”).
The number of applications and tasks running on automotive systems can be staggering when one considers the amount of information being produced from the large collection of sensors, to the data processed and generated by AI systems, to streaming video moving over in-vehicle networks. Managing data distribution in safety-critical areas can benefit from standards like the Object Management Group’s (OMG) Data Distribution Service (DDS) that can provide secure, real-time, publish-subscribe managed data exchange throughout the system (see “Should DDS be the Base Communication Framework for Self-Driving Cars?”). This approach scales better than many point-to-point solutions typically found in designs that require fewer connections between applications.
Read Complete Blog by Clicking below link