A drone trajectory planner should be able to dynamically adjust the safety-efficiency trade-off according to varying mission requirements in unknown environments. Although traditional polynomial-based planners offer computational efficiency and smooth trajectory generation, they require expert knowledge to tune multiple parameters to adjust this trade-off. Moreover, even with careful tuning, the resulting adjustment may fail to achieve the desired trade-off. Similarly, although reinforcement learning-based planners are adaptable in unknown environments, they do not explicitly address the safety-efficiency trade-off. To overcome this limitation, we introduce a Decision Transformer-based trajectory planner that leverages a single parameter, Return-to-Go (RTG), as a \emph{temperature parameter} to dynamically adjust the safety-efficiency trade-off. In our framework, since RTG intuitively measures the safety and efficiency of a trajectory, RTG tuning does not require expert knowledge. We validate our approach using Gazebo simulations in both structured grid and unstructured random environments. The experimental results demonstrate that our planner can dynamically adjust the safety-efficiency trade-off by simply tuning the RTG parameter. Furthermore, our planner outperforms existing baseline methods across various RTG settings, generating safer trajectories when tuned for safety and more efficient trajectories when tuned for efficiency. Real-world experiments further confirm the reliability and practicality of our proposed planner.

Autonomous driving has entered the testing phase, but due to the limited decision-making capabilities of individual vehicle algorithms, safety and efficiency issues have become more apparent in complex scenarios. With the advancement of connected communication technologies, autonomous vehicles equipped with connectivity can leverage vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, offering a potential solution to the decision-making challenges from individual vehicle's perspective. We propose a multi-level vehicle-infrastructure cooperative decision-making framework for complex conflict scenarios at unsignalized intersections. First, based on vehicle states, we define a method for quantifying vehicle impacts and their propagation relationships, using accumulated impact to group vehicles through motif-based graph clustering. Next, within and between vehicle groups, a pass order negotiation process based on Large Language Models (LLM) is employed to determine the vehicle passage order, resulting in planned vehicle actions. Simulation results from ablation experiments show that our approach reduces negotiation complexity and ensures safer, more efficient vehicle passage at intersections, aligning with natural decision-making logic.

Coordination among connected and autonomous vehicles (CAVs) is advancing due to developments in control and communication technologies. However, much of the current work is based on oversimplified and unrealistic task-specific assumptions, which may introduce vulnerabilities. This is critical because CAVs not only interact with their environment but are also integral parts of it. Insufficient exploration can result in policies that carry latent risks, highlighting the need for methods that explore the environment both extensively and efficiently. This work introduces OPTIMA, a novel distributed reinforcement learning framework for cooperative autonomous vehicle tasks. OPTIMA alternates between thorough data sampling from environmental interactions and multi-agent reinforcement learning algorithms to optimize CAV cooperation, emphasizing both safety and efficiency. Our goal is to improve the generality and performance of CAVs in highly complex and crowded scenarios. Furthermore, the industrial-scale distributed training system easily adapts to different algorithms, reward functions, and strategies.

Centralizing domain expertise while having access to detailed inspection data from many remote sites provides BP a new level of operational efficiency. BP uses Formant to collect, consolidate, and visualize all the data obtained during an inspection in a single dashboard. Formant ingests the robot telemetry, video, audio, as well as any data that comes from additional sensors mounted on Spot, such as methane detectors, thermal, infrared, ultraviolet, and hyperspectral data. Viewing all data feeds together, in context, enables operators to compare data that would not normally be contained in a single location. Formant's timeline navigation provides a simple and easy way to review and dive into particular moments of interest, even tagging an event to share with team members.

Right now, the focus is on Spot, the versatile quadruped first made commercially available in June 2020. 'The next big industry for Spot is really in this market that we're calling industrial sensing or dynamics sensing,' Zack Jackowski, chief engineer of the Spot product, told told CNBC over the weekend. '[That's] where we have robots walking around places like manufacturing plants, chemical plants, utilities [and] installations, and using the robots to collect data on what's happening in these facilities in an automated way,' he said. Being able to get repeatable, high quality data from Spot, Jackowski added, could enable companies to boost safety and efficiency in ways they never considered before. While Boston Dynamics likes to play up Spot's softer side -- releasing videos of it playing fetch and dancing to bops by K-pop sensation BTS and other acts -- it's already employed by Hyundai to patrol assembly lines at a Kia factory in Gwangmyeong, Korea. Hyundai has equipped the robot with a thermal camera and three-dimensional LiDAR sensing technology that allows it to see humans, determine whether doors are open or closed, monitor high-temperature systems, and detect fire hazards.

This is an excerpt from the AI case studies bible. Make sure to get your copy now! There is a growing potential and use of AI in the construction industry in real life use cases like designing, planning, and logistics. AI in construction is being used to rethink construction operations in radically new ways. AI in construction leads to time saving, safety and efficiency.

Balancing safety and efficiency when planning in dense traffic is challenging. Interactive behavior planners incorporate prediction uncertainty and interactivity inherent to these traffic situations. Yet, their use of single-objective optimality impedes interpretability of the resulting safety goal. Safety envelopes which restrict the allowed planning region yield interpretable safety under the presence of behavior uncertainty, yet, they sacrifice efficiency in dense traffic due to conservative driving. Studies show that humans balance safety and efficiency in dense traffic by accepting a probabilistic risk of violating the safety envelope. In this work, we adopt this safety objective for interactive planning. Specifically, we formalize this safety objective, present the Risk-Constrained Robust Stochastic Bayesian Game modeling interactive decisions satisfying a maximum risk of violating a safety envelope under uncertainty of other traffic participants' behavior and solve it using our variant of Multi-Agent Monte Carlo Tree Search. We demonstrate in simulation that our approach outperforms baselines approaches, and by reaching the specified violation risk level over driven simulation time, provides an interpretable and tunable safety objective for interactive planning.

New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations. This paper explores how unmanned free-flight traffic may operate in dense airspace. We develop and analyze autonomous collision avoidance systems for aircraft operating in dense airspace where traditional collision avoidance systems fail. We propose a metric for quantifying the decision burden on a collision avoidance system as well as a metric for measuring the impact of the collision avoidance system on airspace. We use deep reinforcement learning to compute corrections for an existing collision avoidance approach to account for dense airspace. The results show that a corrected collision avoidance system can operate more efficiently than traditional methods in dense airspace while maintaining high levels of safety.

Since March, when an autonomous vehicle killed a pedestrian in Arizona, forecasts for AVs have been decidedly less optimistic. But autonomous vehicle promoters are undeterred. AI entrepreneur Andrew Ng contends that self-driving cars will be safe for pedestrians when walkers and cyclists conform to their limitations. "What we tell people is, 'Please be lawful and please be considerate,'" he told Bloomberg. Peter Norton is an associate professor in the Department of Engineering and Society at the University of Virginia. He is the author of Fighting Traffic: The Dawn of the Motor Age in the American City.

Engineers have been spotted testing a prototype drone that should allow Amazon packages to be dropped off safely. The prototype was seen being lowered up and down by a huge crane as tests are carried out to ensure the drone can avoid obstacles and land safely in gardens. Seattle-based Amazon is believed to be testing its sophisticated'sense and avoid' technology at a secret location in the Cambridgeshire countryside. Seattle-based Amazon is believed to be testing its sophisticated'sense and avoid' technology This would enable the drones to eventually fly for ten miles at 400ft (121m) and carry packages of up to 5lbs (2.2kg) to people's homes in under 30 minutes. The size of the drones is unknown, with various shapes and sizes being tested, but some have been estimated as measuring between 17 and 25 inches wide.