Articles tagged with Autonomous Vehicles
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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.
MIT researchers develop a two-dimensional, sub-terahertz receiving array on a chip that's orders of magnitude more sensitive than existing sensors. This can help steer driverless cars through fog and dust by detecting signals at sub-terahertz wavelengths with ease.
Researchers found that programming autonomous vehicles in advance leads to more cooperative decisions, as people prioritize collective interests over short-term rewards. This effect generalizes beyond the domain of autonomous vehicles and has implications for shaping societal dilemmas.
Researchers at George Washington University found that decentralized systems perform better when individual parts are less capable, as overly smart components lead to overcorrections and mistakes. This discovery has potential applications in company structure, autonomous vehicles, AI algorithms, and even biological evolution.
Self-driving cars will slow traffic by cruising at low speeds to avoid high downtown parking fees. Cities face challenges regulating autonomous vehicles, which can create havoc if not properly managed. Congestion pricing is proposed as a solution to reduce congestion and pollution.
The study explores the potential impact of autonomous vehicles on urban tourism, highlighting both benefits such as reduced traffic congestion and improved foreign car hire processes, as well as concerns over job losses and increased urban sprawl. As AVs become mainstream, urban attractions may be transformed, and new industries like A...
A study by CNRS and researchers gathered 40 million moral dilemma responses from web users worldwide. The data revealed global moral preferences guiding decision makers, prioritizing human lives over animals, saving the largest number of lives, and protecting the youngest over older people.
A massive global survey on autonomous vehicle ethics found that people generally prefer sparing human lives over animal lives and young people's lives. However, regional variations were observed, particularly in the eastern cluster of countries where preference for younger people was less pronounced.
Researchers have developed a new material with high birefringence properties that can improve infrared detection in dense fog and dark conditions. This technology may enable faster deployment of autonomous vehicles and create more efficient heat sensing tools for firefighters.
A new lane-change algorithm developed by MIT allows autonomous vehicles to model an entire range of driving styles, from conservative to aggressive, with safety guarantees. The system computes buffer zones on the fly and ensures collision avoidance within a given time frame.
The AVENUE project, led by the University of Geneva, aims to develop and test autonomous vehicle services in European cities. The project will focus on optimizing itineraries, in- and out-of-vehicle services, and addressing security concerns. Geneva is one of the pilot sites for the large-scale tests.
Wiseman and Grinberg propose a system to evaluate collision potential and choose the least harmful course of action for autonomous vehicles. The system uses Spatial Data Structures and Bounding Volumes to analyze possible crashes and decide on the most destructive option.
Researchers at U of T Engineering found that optimizing for autonomous vehicles can increase parking lot capacity by 62%. A well-designed AV parking lot can accommodate more cars than a conventional one, with square-shaped lots capable of increasing capacity up to 87%.
Researchers are developing a miniature collision detection sensor system that could drastically improve the safety of autonomous vehicles. The ULTRACEPT project combines near-range collision detection, long-range hazard perception, and thermal-based collision detection tools to overcome current limitations.
A new study found that autonomous vehicles can minimize environmental impacts with energy-efficient design and operational benefits outweighing increased energy use from sensors and computers. The study showed a 40% reduction in greenhouse gas emissions for electric powertrains versus internal-combustion engines.
A UMass Lowell professor is leading a research study on the ethical dilemmas posed by autonomous vehicles. The team aims to develop decision-making algorithms that balance individual rights with societal welfare. They will also investigate cybersecurity and privacy concerns related to self-driving cars.
New research shows that a small percentage of self-driving cars can significantly impact road flow, eliminating phantom traffic jams and reducing fuel consumption by up to 40%. The study demonstrates the potential for autonomous vehicles to regulate traffic flow and improve efficiency.
A new study suggests that autonomous vehicles could facilitate pedestrian-oriented urban neighborhoods, as pedestrians can act unpredictably and force self-driving cars to stop. However, the adoption of autonomous vehicles may be hampered by their strategic disadvantage in urban traffic.
Researchers at UC Riverside have developed a reliable and accurate navigation system that exploits environmental signals like cellular and Wi-Fi to support autonomous vehicle development. The technology can be used as an alternative or complement to GPS-based systems, enabling consistent and tamper-proof navigation.
A recent study by MIT researchers found that the public takes a utilitarian approach to autonomous vehicle safety, minimizing casualties in extreme situations but prioritizing personal safety. This 'social dilemma' may lead to conditions becoming less safe for everyone as individuals act in their own self-interest.
Researchers at Chalmers University of Technology are developing a self-driving truck that uses animal-inspired behavior to improve safety and adaptability. The truck's software, OpenDLV, is designed to react to unexpected situations by mimicking the way animals respond to their environment.
A novel technique called model predictive path integral control (MPPI) helps autonomous vehicles maintain stability at the edge of handling limits. By leveraging advanced algorithms and onboard computing, MPPI enables vehicles to optimize their trajectories in real-time, reducing the risk of accidents on hazardous roads.
Researchers at UNR are developing software to connect autonomous vehicles with NASA's traffic management system for safe low-altitude operations. The University is part of the first phase of the NASA UTM project to enable safer drone delivery and humanitarian applications.
A new RAND Corporation study suggests that autonomous vehicles will outweigh the likely disadvantages, providing significant social benefits such as decreased crashes, increased mobility, and fuel economy. However, policymakers must address regulatory challenges, liability issues, and privacy concerns.
Researchers at Virginia Tech developed a step-by-step procedure for managing driverless vehicles through intersections, considering factors like location, speed, and acceleration. The proposed system aims to reduce crashes and emissions by keeping vehicles moving and optimizing traffic flow.
Computer scientist Peter Stone is developing a system that enables autonomous cars to coordinate their movements at intersections, reducing stopped traffic. The virtual intersection manager uses AI driver agents to 'call ahead' and reserve space and time, improving safety and efficiency.
Four unmanned autonomous vehicles designed by Virginia Tech engineering students using TORC Robotic Building Blocks product line will be used by the Marine Corps in Hawaii during the RIMPAC war games. The vehicles are equipped with advanced sensors, perception, planning, and control algorithms to navigate complex environments.
A Virginia Tech researcher is developing a fleet of low-cost, miniature autonomous underwater vehicles (AUVs) to collect elusive environmental data in Hog Island Bay. The AUVs will be equipped with sensors to monitor water parameters and work cooperatively to gather data that can't be collected with traditional methods.