Articles tagged with Autonomous Vehicles
Researchers at Purdue University are designing autonomous vehicles that can accommodate people with disabilities. Their goal is to create a standard for technology accessibility that will enable the entire population to use these vehicles, regardless of their mobility or sensory challenges.
Researchers have demonstrated a power-efficient component for demultiplexing operation using silicon photonic MEMS, enabling efficient wavelength demultiplexing for fiber-optic communications. The compact footprint of the add-drop filter allows fast operation compared to established MEMS products.
Researchers at Purdue University and the University of Tennessee, Knoxville, have developed a metamaterial that can learn to adapt to its surroundings on its own. The material uses shape to store information in microseconds, allowing drones to quickly recall patterns associated with dangerous conditions.
A new study at the University of Tokyo suggests that robotic eyes on autonomous vehicles can help pedestrians anticipate a vehicle's intentions, leading to safer crossings. The experiment found that participants made more cautious decisions when faced with moving eyes, with some males even reporting feeling safer.
Researchers developed a silicon photodiode array for in-sensor processing, allowing for real-time image filtering and extraction of relevant visual information. The technology has potential applications in machine vision, bio-inspired systems, and intelligent imaging devices.
Researchers at NC State University have developed a cooperative distributed algorithm that allows autonomous vehicle software to make calculations more quickly, enabling real-time navigation of complex merging scenarios. The approach improves both traffic flow and safety, with zero incidents in simulations.
A recent study published in Nature Scientific Reports has found that women are better at taking over control of automated cars compared to men. The research involved 76 drivers who participated in a driving simulator study and showed that women exhibited faster reaction times and more stable operation of the steering wheel.
The Impact Acceleration Account supports critical early-stage translation of UK research to transform public services, create new businesses, and jobs. The programme provides funding to unlock the value of UK research, including commercialisation of new technologies.
A new AI system uses artificial neural networks to recognize objects more accurately and stably, despite changing visual inputs. The system mimics human eye movements to improve machine vision capabilities, reducing errors in self-driving cars and other applications.
A new optimization tool can quickly improve performance of various autonomous systems, including walking robots and self-driving vehicles. The tool uses automatic differentiation to identify tweaks that achieve desired outcomes, reducing trial-and-error simulations.
Researchers from Carnegie Mellon University gathered data on an all-terrain vehicle's interactions with challenging off-road environments, collecting over 200,000 real-world interactions. The resulting TartanDrive dataset is multimodal and includes information about speed, suspension shock travel, and video.
Researchers at MIT developed a technique that enables an autonomous vehicle to plot a provably safe trajectory in highly uncertain situations. The algorithm considers probability of observing different environmental conditions and obstacles, and formulates trajectory planning as a probabilistic optimization problem.
A new study demonstrates a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection. The technique reduces fuel consumption and emissions while improving average vehicle speed, with benefits seen even when only 25% of cars use the control algorithm.
Researchers at Stevens Institute of Technology developed an underwater robot capable of mapping its environment, tracking its location, and planning safe routes in complex marine environments. The breakthrough enabled the robot to accurately map a busy harbor in real-time using active SLAM algorithms.
Researchers at Tokyo University of Science propose two new search strategies to reduce the computational cost of rebalancing in bicycle-sharing systems. The approaches focus on finding feasible solutions more efficiently and redefine the problem to minimize solving time.
Researchers at Duke University have demonstrated a new attack strategy that can deceive industry-standard autonomous vehicle sensors into believing nearby objects are closer or further than they appear. This vulnerability highlights the need for additional redundancy and data sharing between vehicles to protect against such attacks.
Researchers developed MonoCon, a new AI technique that enables accurate identification of 3D objects in 2D images. By incorporating auxiliary context, the method improves object detection and estimation accuracy, paving the way for safer and more robust autonomous vehicles.
The MIT team developed a levitating rover design using ion thrusters, capable of producing enough repulsive force to hover on the moon and larger asteroids. The concept uses tiny ion beams to charge up the vehicle and boost its natural surface charge.
A team of UBC Okanagan researchers has developed a system to enhance interactions between humans and robots in industrial settings. The system uses artificial intelligence and machine learning to capture and analyze the environment, allowing robots to respond in a timely manner to ensure human safety.
The grant aims to build a robot that navigates complex environments and collects data over long periods, combining image collection and acoustic analysis to mimic a trained diver's curiosity. This will enable scientists to slowly build a detailed picture of ecosystem function and health, reducing the impact on animals' behavior.
Experts call for a national compensatory body and guarantee fund to compensate victims of mass-hacking, citing potential catastrophic consequences and financial losses. The UK has an opportunity to establish itself as a leading role in connected and autonomous vehicle development by creating an insurance fund.
The Quantum Sensors project aims to create ultrasensitive gyroscopes and accelerometers using quantum states, enabling precise measurements for self-driving cars and spacecraft. This technology could capture information not provided by GPS, improving navigation and stability in various environments.
A blockchain-based system allows leader robots to signal movements and add transactions to a chain, while malicious leaders forfeit tokens when caught in a lie. This limits the spread of incorrect information and enables follower robots to eventually reach their destination.
Researchers used neuroscientific theories to develop a decision-making model that predicts pedestrian road-crossing decisions. The model shows that pedestrians add up sensory data before crossing, helping autonomous vehicles communicate more effectively with pedestrians.
A North Carolina State University panel of experts assesses AV deployment risks and benefits, predicting substantial benefits if well-regulated. The study's findings suggest regulations could limit urban use of AVs and commercial fleet ownership, reducing risk and increasing benefits.
Researchers found that robots and vehicle automation account for a small percentage of greenhouse gas emissions, with delivery vehicle size and fuel source having larger impacts. A human-driven, battery-powered cargo van has the lowest per-package footprint among analyzed methods.
The new infrared detector can make two technically important ranges of infrared radiation visible, previously not covered by conventional photodiodes. The sensor can distinguish between substances based on their different absorption properties in the NIR and SWIR range.
Researchers at MIT have created an algorithm that enables drones to navigate complex obstacle courses at high speeds without crashing. The new approach combines simulations with real-world experiments, allowing drones to adapt to challenging aerodynamics and find the fastest routes.
A research team developed a novel sensor that can detect and convert infrared light into electrical signals at temperatures up to 100°C. The device eliminates the need for cooling devices, reducing production costs and operating speeds, making it suitable for smartphone and autonomous vehicle applications.
Researchers at Carnegie Mellon University developed an algorithm that can assist autonomous vehicles in navigating tight spaces and unknown driver intentions. The algorithm was trained on simulation data and found to perform better than current models in handling complex scenarios like bi-directional lane usage.
Researchers developed a method to transform uncertainty-unaware controller models into robustified models that can safely behave under uncertainty. The method generates formulas representing the degree of uncertainty a controller can tolerate, enabling flexible analysis and consideration of real-world deployment situations.
Researchers at Florida Atlantic University have developed a technology that uses non-intrusive sensors to perceive drivers' and passengers' moods and adjust driving modes accordingly. This innovation addresses major issues with autonomous vehicles, such as accurately predicting human behavior.
Researchers at Michigan Technological University discuss solutions for snowy driving scenarios using sensor fusion, which combines data from various sensors like lidar, radar, and cameras. This approach enables autonomous vehicles to better detect obstacles and understand their environment.
A study found that consumers are concerned about autonomous vehicles due to performance risks, loss of driving skills and privacy security breaches. Despite these concerns, many see benefits such as freeing time, removing human error and improved performance.
Researchers have developed a new, pixel-sized light detector that can accurately amplify weak signals in real-time, giving autonomous vehicles a fuller picture of their surroundings. This breakthrough increases sensitivity and consistency, making it ideal for lidar receivers and applications in robotics, surveillance, and terrain mapping.
New research reveals autonomous vehicles will increase energy consumption and greenhouse gas emissions across all categories studied. However, electric autonomous vehicles can change this outcome, particularly if adoption rates exceed 40%.
A new study by computer scientists found that individuals are more willing to sacrifice their own safety when peers are more likely to do so in programming autonomous vehicles. The researchers also showed that the social component of decision-making is often overlooked and that transparency in machine programming is crucial for public ...
The Autonomous Vehicle Assistant (AVA) smartphone app provides navigational assistance for people with disabilities and seniors to use self-driving cars. The app offers a multisensory interface, GPS technology, real-time computer vision, and artificial intelligence to support navigation and vehicle access.
Researchers developed a novel control architecture that defends complex, interconnected systems against cyberattacks by implementing a Leader-Follower approach. This algorithm can detect and isolate infected sub-systems, reducing the impact of targeted DoS attacks and increasing system robustness.
The University of Texas at Arlington is launching a public transportation pilot program with free rides for students, featuring autonomous vehicles provided by May Mobility. The program aims to develop efficient, safe, and accessible transit networks in low-density settings.
Researchers at TUM have developed a software module that analyzes and predicts events while driving, ensuring the vehicle will not cause accidents. The system determines movement options and emergency maneuvers to prevent collisions, using simplified dynamic models for swift calculations.
Researchers at the University of Warwick have developed a training tool to reduce motion sickness susceptibility by over 50% using visuospatial exercises. This method has shown promise in both driving simulators and on-road experimentation, with potential applications in autonomous vehicles and other domains.
Cyber-physical systems face challenges due to large volumes of data flowing through communication networks, causing routing and queuing delays that degrade system quality. The new algorithms developed by researchers strike a balance between communication sparsity, delay, and performance, ensuring safe and stable operation.
A North Carolina State University expert suggests using a framework that considers three variables: agent intent, deed, and consequence. This approach can lead to more nuanced moral judgments, similar to human moral judgment.
Researchers have developed a new optimization method for tracking self-driving car trajectories that reduces errors while keeping computation demands low. The method prioritizes passenger comfort, aiming to replicate human drivers' ability to balance speed, safety, and route choice.
Researchers at Northwestern University developed a decentralized algorithm that enables swarms of robots to form shapes and avoid collisions. The algorithm views the ground as a grid, allowing each robot to sense its closest neighbors and make local decisions without global information.
The study found that autonomous vehicles can reduce road accidents and promote physical activity, but the impact depends on factors like technology implementation and fuel use. If implemented correctly, autonomous vehicles could have a significant positive impact on public health, especially if used in electric and shared formats.
Researchers have developed bioinspired shapes that serve as acoustically conspicuous guideposts for sonar-guided autonomous vehicles. These bioinspired sonar reflectors were tested in experiments and showed promising results, enabling robots to navigate through new environments with improved accuracy.
Researchers have developed a new model to help autonomous vehicles navigate intersections with obstructed views, reducing the risk of collisions. The model estimates risks based on factors like sensor noise, driver awareness, and occlusions.
The researchers have developed a platform that enables companies to quickly address safety concerns in autonomous vehicles through software and hardware advances. They analyzed 144 AVs driving over 1,116,605 miles and found that human-driven cars were up to 4000 times less likely than AVs to have an accident.
New research from the University of Adelaide predicts that driverless cars could increase traffic congestion in Australia's City of Adelaide. The study, which surveyed over 500 commuters, found that autonomous vehicles may lead to an adverse impact on public transport, causing peak period vehicle flows to rise.
A new decision-making and motion-planning technology has been developed to limit injuries and damage when self-driving vehicles are involved in unavoidable crashes. The system considers various factors such as relative speeds, angles of collision, and differences in mass and vehicle type to determine the best possible manoeuvre.
A four-year study will examine the impacts of autonomous vehicles on the future workforce, focusing on how driving jobs will change and what new skills are required. Researchers will also investigate the anticipated downstream impacts on drivers' employment trends and income inequality.
A new algorithm assesses cloudlet placement to achieve an optimal balance between cost and service performance. The proposed method uses cloudlets, tiny versions of the cloud, to improve network service performance for a limited number of users.
A new study aims to explore ways to share decision-making and information between humans and AI drivers, addressing concerns about perceived loss of control and fear of unknown actions. The goal is to increase user trust, consumer acceptance, and overall usability of autonomous vehicles.
A study by the University of Warwick found that neither fully machine-like nor human-like autonomous vehicle driving styles were optimal, but a blend of both may be best. Passengers showed increased confidence with each run in both styles.
In the INFORMS O.R. & Analytics Student Team Competition, student teams from around the world used operations research (OR) and analytics to optimize autonomous vehicle delivery processes for General Motors. The winning team from Korea Advanced Institute of Science and Technology minimized total costs while satisfying all constraints.
A new mathematical method developed by USC researchers can identify anomalies in self-driving cars' perception algorithms before they hit the road, improving safety. The method uses 'sanity conditions' to test machine learning tools and can be used to pinpoint specific problems and retrain the algorithms for faster error detection.
Well-managed autonomous vehicles could increase mobility, improve safety, and reduce emissions, but poorly managed ones could exacerbate environmental problems. Implementing fuel economy standards and regulations can ensure a positive outcome.
A recent special issue published in Risk Analysis examines how attitudes and perceptions of risk affect the acceptance of autonomous vehicles. Studies found that most people prefer defaults to staying in their lane when faced with a collision, and social trust plays a significant role in public acceptance.