Collaborating Authors


Scenario-based simulation: Combining HD maps and real-world traffic data - atlatec


If you work in the ADAS/Autonomous Vehicles field, you are probably familiar with HD maps โ€“ virtual recreations of real-world roads including their 3D profile, driving rules, inter-connectivity of lanes etc. A lot of these HD maps go into the simulation domain, where car makers and suppliers leverage them to train new ADAS/AV systems or for verification/validation of features from those domains. The reason to use HD maps of real-world roads (rather than just generic, fictional routes created from scratch) is simple: In the end, you want your system to perform in the real world โ€“ so you want to optimize for real-world conditions as early as possible, starting in simulation. As we all know, the real world is nothing if not random, and you will encounter many situations you would rarely find in generic data sets. So far, so good: These HD maps can be used to properly train lane-keep assistance or lane-departure warning systems, validate speed limit sign detection and many other systems. However, a map only contains the static features of an environment โ€“ what about ADAS/AV features that are supposed to react to other traffic participants?

When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans Artificial Intelligence

In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and assume users are always optimal. Other robots account for human limitations, and relax this assumption so that the human is noisily rational. Both of these models make sense when the human receives deterministic rewards: i.e., gaining either $100 or $130 with certainty. But in real world scenarios, rewards are rarely deterministic. Instead, we must make choices subject to risk and uncertainty--and in these settings, humans exhibit a cognitive bias towards suboptimal behavior. For example, when deciding between gaining $100 with certainty or $130 only 80% of the time, people tend to make the risk-averse choice--even though it leads to a lower expected gain! In this paper, we adopt a well-known Risk-Aware human model from behavioral economics called Cumulative Prospect Theory and enable robots to leverage this model during human-robot interaction (HRI). In our user studies, we offer supporting evidence that the Risk-Aware model more accurately predicts suboptimal human behavior. We find that this increased modeling accuracy results in safer and more efficient human-robot collaboration. Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI.

Modelling Bushfire Evacuation Behaviours Artificial Intelligence

Bushfires pose a significant threat to Australia's regional areas. To minimise risk and increase resilience, communities need robust evacuation strategies that account for people's likely behaviour both before and during a bushfire. Agent-based modelling (ABM) offers a practical way to simulate a range of bushfire evacuation scenarios. However, the ABM should reflect the diversity of possible human responses in a given community. The Belief-Desire-Intention (BDI) cognitive model captures behaviour in a compact representation that is understandable by domain experts. Within a BDI-ABM simulation, individual BDI agents can be assigned profiles that determine their likely behaviour. Over a population of agents their collective behaviour will characterise the community response. These profiles are drawn from existing human behaviour research and consultation with emergency services personnel and capture the expected behaviours of identified groups in the population, both prior to and during an evacuation. A realistic representation of each community can then be formed, and evacuation scenarios within the simulation can be used to explore the possible impact of population structure on outcomes. It is hoped that this will give an improved understanding of the risks associated with evacuation, and lead to tailored evacuation plans for each community to help them prepare for and respond to bushfire.

A 20-Year Community Roadmap for Artificial Intelligence Research in the US Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.

Why Artificial Intelligence is scaring everyone


By their own admission, Jack Ma is uncomfortable with Artificial Intelligence (AI) and Elon Musk is scared. But why? Contrary to popular perception AI is old. To be precise it's 51-years old, widely acknowledged to have been born at a conference at Dartmouth College in 1956. That conference was attended by a diverse group of people. Three of them presented the Logic Theorist, the world's first true artificial intelligence programme.