The introduction sets the stage for exploring collaborative approaches to bolstering smart vehicle cybersecurity through AI-driven threat detection. As the automotive industry increasingly adopts connected and automated vehicles (CAVs), the need for robust cybersecurity measures becomes paramount. With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies such as 5G networks, blockchain, and quantum computing presents promising avenues for enhancing CAV cybersecurity . Additionally, the roadmap for cybersecurity in autonomous vehicles emphasizes the importance of efficient intrusion detection systems and AI-based techniques, along with the integration of secure hardware, software stacks, and advanced threat intelligence to address cybersecurity challenges in future autonomous vehicles.

Abstract--Increasingly, autonomous vehicles (AVs) are becoming a reality, such as the Advanced Driver Assistance Systems (ADAS) in vehicles that assist drivers in driving and parking functions with vehicles today. The localization problem for AVs relies primarily on multiple sensors, including cameras, LiDARs, and radars. Manufacturing, installing, calibrating, and maintaining these sensors can be very expensive, thereby increasing the overall cost of AVs. This research explores the means to improve localization on vehicles belonging to the ADAS category in a platooning context, where an ADAS vehicle follows a lead'Smart' AV equipped with a highly accurate sensor suite. We propose and produce results by using a filtering framework to combine pose information derived from vision and odometry to improve the localization of the ADAS vehicle that follows the smart vehicle.

Too Long; Didn't Read 7.3% of incidents affecting connected vehicles between 2010 and 2021 involved a companion mobile app. Cyberattacks on vehicles increased 225% in 2021 from 2018, while threat actors were responsible for 54.1% of the incidents. Keyless entry and key fob attacks accounted for 50% of all vehicle thefts. Emerging threat vectors are proving to be very disruptive. Threat actors use charging stations to attack electric vehicles, commit impersonation fraud, and disrupt the ability to charge electric vehicles at scale.

New technology is giving autonomous vehicles'X-ray' vision to help them track pedestrians, cyclists and other vehicles that may be obscured. Experts in Australia are now commercialising the technology, which is called cooperative or collective perception (CP). It involves the installation of roadside information-sharing units ('ITS stations') equipped with sensors such as cameras and lidar. At a busy junction, for example, vehicles would use these units to share what they'see' with other vehicles. This gives each vehicle X-ray style vision that sees through buses to notice pedestrians, or a fast-moving van around a corner that's about to run a red light.

Chinese electric vehicle startup Xpeng is targeting a younger demographic with its latest invention: a robot unicorn children can actually ride. The'Little White Dragon' will be equipped with an artificial intelligence that allows it to maneuver autonomously, navigate various terrains and recognize people and objects, and provide'intelligent emotional interaction,' according to The South China Morning Post. Like a real unicorn, this four-legged wonder doesn't seem to exist--not yet, anyway. Xpeng released a computer-animated video on Tuesday showing the Little White Dragon playing with a young boy. The company's robotics division hopes the creature will become the first smart vehicle for children and launch'a robotic ecosystem' based on the company's smart-car business, according to SCMP.

Over 72.5 million connected car units are estimated to be sold by 2023, enabling nearly 70% of all passenger vehicles to actively exchange data with external sources. The amount of data resulting from these smart vehicles will be overwhelming for traditional data processing solutions to gather and analyze, as well as the associated latency of processing this data-- leading to potential life-or-death scenarios, according to Ramya Ravichandar, from Foghorn. We speak with Ravichandar, about how connected car manufacturers are implementing edge AI solutions for real-time video recognition, multi-factor authentication, and other innovative capabilities to decrease network latency and optimize data gathering, analyzing and security. Digital Journal: What are the current trends with autonomous and connected cars? Ramya Ravichandar: Automotive companies are looking to improve real-time functionalities and accelerate autonomous operations of passenger vehicles. Connected vehicle technology is introducing a new dimension of transportation by extending vehicle operations and controls beyond the driver to include internal networks and systems.

Understanding the Quality of Experience (QoE) for visual media has been very important for optimal compression and content delivery for diverse video formats, and it has been a hot topic of research for the last decades. Researchers mostly studied this problem from a signal processing perspective using image and video processing tools, while learning-based methods have been increasing momentum recently. The popularisation of deep learning-based methods affected the whole signal and image processing community as a disruptive force, and visual QoE estimation is no different than others. Use of learning-based methods and especially deep learning methods open a new path for understanding the human visual system in the perception process and other QoE parameters. The objectives of this special session are twofold: first, to develop new metrics reaching beyond the performance of the legacy signal processing approaches for visual QoE estimation, and second, to understand the stages of human perception for visual media better utilising the learning-based methods and different analysis methods such as ablation studies.

As car companies increasingly tout semi- and fully-autonomous features -- including lane control and "autopilot" -- and 29 states have enacted legislation related to self-driving vehicles, UC Berkeley transportation researchers are addressing this emerging era of smart vehicles with a tool that uses machine learning to manage traffic where autonomous, semi-autonomous and manned vehicles share the road. The project, called Flow, rolled out its first proposed standards for solving real-world traffic problems, including easing bottlenecking on the San Francisco-Oakland Bay Bridge, today (Monday, Oct. 29) at the Conference on Robotic Learning in Zurich, Switzerland. Many traffic researchers are addressing smart-vehicle integration, but compared to models that use manually derived algorithms to design controls like metering-light timing, machine-learning-based controls can provide benefits like lower energy consumption and novel traffic-management solutions that are out of reach of human calculations. "Flow solves large-scale, multi-vehicle problems by using simulations that are much more efficient than what can be produced without the aid of artificial intelligence," said electrical engineering and computer sciences professor Alexandre Bayen, director of the UC Berkeley Institute of Transportation Studies and the study's principal investigator. "And we've made it a cloud-based, open-source system so the development community can continue to build on it."

John Ludwig is an electrical engineer and the president of Xevo's Artificial Intelligence (AI) Group. Xevo is a tier-one OEM software company, located in Seattle, that manages automotive software for driver assistance, engagement, and in-vehicle entertainment. Its main product is the Xevo Market, a merchant-to-driver commerce platform that uses a vehicle's infotainment screen to make purchases and transations from inside the car. Xevo Market launched at the end of 2017 and is already available in millions of vehicles. Prior to working with Xevo, Ludwig was a software manager with Microsoft, overseeing operating systems and online service projects.

EPFL and Nissan researchers are able to read a driver's brain signals and send them to a smart vehicle so that it can anticipate the driver's moves and facilitate the driving process. Nissan recently unveiled this brain-to-vehicle (B2V) technology. Future cars will be both self-driving and manual. "We wanted to harness technology to enhance drivers' skills without interfering with the enjoyment of being behind the wheel," explains José del R. Millán, who holds the Defitech Foundation Chair in Brain-Machine Interface (CNBI). As part of a joint project with Nissan researchers based at the CNBI, the team managed to read the brain signals that indicate a driver is about to do something – such as accelerate, brake or change lanes – in order to send that information to the vehicle.