Artificial Intelligence (AI) in Automotive Market Size
Artificial intelligence (AI) in automotive industry is expected to cause a profound disruption by streamlining production capabilities and augmenting business growth. The design and deployment of novel technologies including autonomous mobility, vehicle simulations, rapid prototyping, and AI-enabled automotive factories are creating a positive outlook for the autonomous technologies market. Auto manufacturers are rapidly upgrading their existing production systems by incorporating AI platforms and are increasingly focusing on the development of autonomous vehicles to improve passenger mobility. For instance, in October 2019, Tesla announced the early release of its fully autonomous vehicles by Q1 2020 for capturing early mover advantage.
The AI in automotive market growth is attributed to the increasing popularity of autonomous vehicles for increased driver comfort and reducing accidents. The U.S. National Highway Traffic Authority has estimated that the deployment of autonomous vehicles could save USD 300 billion by 2025, reducing collisions and enabling safe driving. The increasing positive perception among consumers regarding autonomous technologies will complement the market revenue. As consumers are rapidly adopting driver-assist technologies to reduce driving burden and ensure road safety, the demand for autonomous vehicles will surge prolifically over the forecast timespan. Automotive manufacturers are taking note of these trends and are offering innovative driver assistance solutions to gain a competitive advantage. In March 2019, BMW and Daimler entered into a strategic collaboration to develop new ADAS solutions and establish industry standards to influence future regulations on driver assistance technologies. Another factor contributing to the AI in automotive market share is the increasing demand for driver convenience and improving access to mobility. Autonomous technologies can help persons with disabilities and senior citizens have access to self-driven mobility, reducing long-distance truck & bus driver fatigue. For instance, in March 2018, the Netherlands government announced a partnership with Belgium and Germany to introduce truck platooning corridors, facilitating the movement of 100 self-driving trucks during the night. Similarly, in November 2018, Singapore announced the launch of driverless buses & shuttles to reduce peak traffic loads starting 2022. However, expensive autonomous technologies, issues concerning accidents of self-driving cars, and the risks of cyber-attacks on connected vehicles may hinder the market demand from 2020 to 2026.
Growing uptake of AI optimized GPUs augmenting the hardware segment
The adoption of AI hardware will witness a rapid surge to enable self-driving technologies and improve AI algorithms through dedicated AI-enabled GPUs. The increasing importance of perception sensors including high-resolution cameras, LiDAR, and radar in providing accurate localization & context awareness to onboard AI systems is accentuating the growth of the hardware segment. Furthermore, the development of AI optimized processors and computing hardware will enable enterprises to design and deploy improved autonomous solutions. For instance, in September 2019, Horizon Robotics, a leading AI computing company, introduced its 2nd generation automotive AI processor – Horizon Journey 2. This new solution is helping automotive manufacturers to incorporate next-generation ADAS capabilities and intelligent cockpit experiences with limited power consumption and high efficiency.
Extensive usage of deep learning for improving self-driving algorithms
Rijkswaterstaat is an executive agency of the Ministry of Infrastructure and Water Management dedicated to promote safety, mobility and the quality of life in the Netherlands. A research has been carried out to identify the market developments of Advanced Driver Assistance Systems (ADAS), to interpret them for Rijkswaterstaat as a road authority. It is currently about SAE 1 and 2 vehicles. Ultimately, the growth of the higher SAE classes and fully automatic vehicles and their effects on the road network and traffic flow will also be monitored. A factsheet on “Slimme Voertuigen” and a background report “Ontwikkelingen en cijfers veiligheids – en comfortsystemen wagenpark 2019” consider market and fleet developments and the effect on road safety of ADAS systems in recent years. Rijkswaterstaat looked at new sales and the ADAS share in the total fleet of passenger cars. The focus has been on ADAS systems to improve traffic safety.
Based on a data analysis for a selection of ADAS systems, the growth in new sales of passenger cars in the Netherlands was examined (2012 to 2018). In addition, a sample of 7,500 cars was used to determine what percentage of the systems offered as an option were purchased. Finally, using the data analysis and investigations of several safety and car-related institutes and insurance organizations (SWOV, EuroNCAP, BOVAG, GDV, IIHS, HLDI), a Top5 of safety-related ADAS was compiled. The findings are set out in the factsheet and background report.
ADAS and road safety
Advanced Driver Assistance Systems (ADAS) can provide personal assistance in a road environment that cannot always take into account the possibilities and limitations of the older driver. An analysis of the strengths and weaknesses of the older driver has shown that the most important need for support stems from the difficulties that older drivers have to:
- Judge whether fellow road users are approaching the same intersection and at what speed
- Notice other road users while merging and changing lanes
- Notice traffic signs and signals
- React quickly in a complex traffic situation.
These difficulties stem from functional limitations such as a decrease in motion perception, peripheral vision, flexibility of head and neck, selective attention, and speed of processing information and decision making. ADAS that can compensate for these limitations, can contribute to a reduction of the crash involvement of older drivers. Such ADAS should have one or more of the following functionalities :
- Draw attention to approaching traffic
- Signal road users located in the driver's blind spot
- Assist the driver in directing his attention to relevant information and/or
- Provide prior knowledge on the next traffic situation .
ADAS that have these functionalities could improve the safety of older drivers. Examples of such systems are collision warning systems aimed at intersections and in-vehicle signing systems. There are, however, also ADAS that could improve the mobility of the older driver, or may reduce his injury severity. Examples of those systems are vision enhancement systems and mayday systems.
Using ADAS to improve the safety or mobility of the driver involves more than making sure that the supported subtask is carried out safely. It also involves that the support provided does not have any negative effects on the other elements of the driving task. Examples of negative side effects are increased task load due to a bad design of the human machine interface, and the effects of behavioural adaptation. ADAS that could improve road safety for older drivers
Regulations for on-road testing of connected and automated vehicles: Assessing the potential for global safety harmonization
Regulations for on-road testing of connected and automated vehicles: Assessing the potential for global safety harmonization
USING TELEMATICS IN PROFESSIONAL VEHICLE FLEETS
A range of measures has been introduced to try and tackle this problem. Governments have updated legislation, clarifying the obligations of employers concerning the safety and well-being of their employees and ensuring that road safety is an integral part of this. Health and safety authorities have issued improved guidance for organisations, to help them abide by their legal responsibilities but also encouraging them to go beyond these requirements and be proactive in managing road safety for their workers.
Finally, organisations have taken up the mantle of road safety themselves. A plethora of road safety schemes, initiatives and programmes has been implemented with the aim of enhancing the safety of employees, but also reducing costs and improving efficiency. These include driver training, group discussions, incentives, publicity campaigns and improved management of road safety across the organisation.
Recently, there has been a growth in the use of invehicle data recorders, also known as telematics systems, to monitor and collect data on an individual’s driving. Some are used to capture data in the period immediately preceding a collision, but increasingly they are being used to continually monitor driving behaviour. The detailed data that is collected can then be used for a number of purposes, such as feedback sessions, future driver training, the calculation of incentives, and the identification of potential fleet-wide issues.
Road safety with C-V2X – at a four-way ITS intersection, with no traffic signals
Car companies Audi, Ducati, and Ford have joined with California chip maker Qualcomm to prove the “maturity and potential” of cellular vehicle-to-everything (C-V2X) technology by showing their vehicles navigating a crossroads by themselves.
The showcase at the 2019 Consumer Electronics Show (CES) in Las Vegas presented a “cooperative intersection” within an ‘intelligent transport system’ (ITS) mock-up, where the safety of the vehicles – variously, Audi and Ford motor cars, and a Ducati motorbike – was governed by their C-V2X links with other vehicles, pedestrians, and road infrastructure.
All vehicles were equipped with Qualcomm 9150 C-V2X chipsets, and guided towards a four-way intersection, featuring no conventional lights or signals. Between them, each vehicle negotiated its course, and shared non-line-of-sight (NLOS) conditions.
The quartet exhibited use-cases of vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P) and vehicle-to-infrastructure (V2I) safety scenarios.
The V2V use case featured the ‘intersection movement assist’ (IMA) scenario, where a vehicle is warned it is not safe to enter an intersection. IMA addresses angle collisions at intersections.
The V2P scenario exhibited how C-V2X can be used to protect vulnerable road users, including pedestrians and bicyclists. The V2I use case featured a so-called ‘work zone warning’ scenario, where the vehicle is warned to use caution because of road construction ahead.
Don Butler, executive director at Ford for connected vehicle platform, commented: “Imagine a person suddenly steps into the street from out of view and your vehicle stops because it was alerted by that person’s smartphone. This is just one example of how C-V2X can connect smart vehicles to a smart world.”
Anupam Malhotra, director of connected vehicles at Audi in the US said: “The four-way stop use case provides a glimpse of how cooperative driving will be possible with C-V2X in the future. Besides additional safety, C-V2X is designed to also improve comfort and efficiency, which can be applied to both current and future autonomous driving.”
Insurance 2030—The impact of AI on the future of insurance
Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. His digital personal assistant orders him an autonomous vehicle for a meeting across town. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into “active” mode. Scott’s personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott’s assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a “pay-as-you-live” basis, will increase by 2 percent for this quarter. The additional amounts are automatically debited from his bank account.
When Scott pulls into his destination’s parking lot, his car bumps into one of several parking signs. As soon as the car stops moving, its internal diagnostics determine the extent of the damage. His personal assistant instructs him to take three pictures of the front right bumper area and two of the surroundings. By the time Scott gets back to the driver’s seat, the screen on the dash informs him of the damage, confirms the claim has been approved, and that a mobile response drone has been dispatched to the lot for inspection. If the vehicle is drivable, it may be directed to the nearest in-network garage for repair after a replacement vehicle arrives.
While this scenario may seem beyond the horizon, such integrated user stories will emerge across all lines of insurance with increasing frequency over the next decade. In fact, all the technologies required above already exist, and many are available to consumers. With the new wave of deep learning techniques, such as convolutional neural networks,1 artificial intelligence (AI) has the potential to live up to its promise of mimicking the perception, reasoning, learning, and problem solving of the human mind (Exhibit 1). In this evolution, insurance will shift from its current state of “detect and repair” to “predict and prevent,” transforming every aspect of the industry in the process. The pace of change will also accelerate as brokers, consumers, financial intermediaries, insurers, and suppliers become more adept at using advanced technologies to enhance decision making and productivity, lower costs, and optimize the customer experience.
Tesla could beat Waymo in the race to self-driving future
Autonomous driving technology is currently in its infancy, but evolving rapidly. More companies are testing autonomous vehicles than ever. Tesla and Waymo are two prominent names competing to capitalize on the opportunity of becoming the industry leader in the self-driving future, but employ different technologies to achieve those goals.
LiDAR stands for Light Detection and Ranging, which is a system that uses lasers for detection. There are sensors on-board that detects the reflected laser light and measures the distance to a target.
A computer vision system uses cameras to capture the environment and processes images in real-time through a machine learning neural network.
Advantages of Computer Vision
The main benefit of a camera based system is that it costs a lot less to install cameras on a car and it can be trained through computer learning to recognize objects. With this system you can drop the car in an unfamiliar environment and it could still find its way.
Can read traffic signs
Another benefit of a camera based system allows the car to read signs. The current road infrastructure is set up for human vision and the camera system is based on this vision. It read signs in real-time and respond accordingly, like a human would.
Looks like a regular car
There are no odd looking domes popping out of the body of a car in camera based setup. The built-in cameras allows for a very sleek design which preserves the aesthetics of the car.
Maybe the only disadvantage of this system is that it takes a lot more machine learning and software development, requiring a lot more computing power.
Beating Google, Tesla, this startup's self-driving truck is all set to hit Indian roads Read more at: https://economictimes.ind
Beating Google, Tesla, this startup's self-driving truck is all set to hit Indian roads
While the global giants may have decided to stay away from the dusty and the pothole-ridden roads of India in their race to be hailed as the pioneers of self-driven technology; a young Bengaluru-based startup believes the chaotic, poorly-kept roads of India are ideal grounds for them to master their self-driving technology.
"For us, the more chaotic the roads are, the more data we get and the more trained our algorithm becomes. Indian roads are helping us to make a far sturdier and soph ..