Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations Artificial Intelligence

This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios. Simulation is used to re-create a targeted driving situation, one containing a road-side hazard creating a significant occlusion in an urban neighborhood, and collect optimal driving behaviors from 24 users. Paper employs a key-frame based approach combined with an algorithm to linearly combine models in order to extend the behavior to novel variations of the target situation. This approach is theoretically agnostic to the kind of LfD framework used for modeling data and our results suggest it generalizes well to variations containing an additional number of hazards occurring in sequence. The linear combination algorithm is informed by analysis of driving data, which also suggests that decision-making algorithms need to consider a trade-off between road-rules and immediate rewards to tackle some complex cases.

How the IoT is keeping traffic moving and the streetlights shining


The next time your car bottoms out on a nasty pothole, grit your teeth and try to spare a thought for the people trying to end that problem and smooth out your journey. Yotta helps local authorities and utility companies understand their infrastructure - like roads and streetlights - better by surveying and analysing the environment. ZDNet talked to Manish Jethwa, Yotta's chief product and technology officer, to find out more. ZDNet: How would you describe Yotta and the business you are in? Jethwa: We're a technology business that has been around for some 25 years in the highways arena.

SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensing Machine Learning

World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of traffic data has been produced by people, Internet of Vehicles and Internet of Things (IoT). Also, thanks to advances in cloud computing and proliferation of mobile communication technologies, it is now possible to perform analysis on vast amount of generated data (crowd sourced) and deliver the result back to users in real time. This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time. SafeRNet utilizes Bayesian network to formulate safe route model. Furthermore, a case study is presented to demonstrate the effectiveness of our approach using real traffic data. SafeRNet intends to improve drivers safety in a modern technology rich transportation system.

Interaction-Aware Probabilistic Behavior Prediction in Urban Environments Artificial Intelligence

Planning for autonomous driving in complex, urban scenarios requires accurate trajectory prediction of the surrounding drivers. Their future behavior depends on their route intentions, the road-geometry, traffic rules and mutual interaction, resulting in interdependencies between their trajectories. We present a probabilistic prediction framework based on a dynamic Bayesian network, which represents the state of the complete scene including all agents and respects the aforementioned dependencies. We propose Markovian, context-dependent motion models to define the interaction-aware behavior of drivers. At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference. Secondly, we perform a probabilistic forward simulation of the network's estimated belief state to generate the different combinatorial scene developments. This provides the corresponding trajectories for the set of possible, future scenes. Our framework can handle various road layouts and number of traffic participants. We evaluate the approach in online simulations and real-world scenarios. It is shown that our interaction-aware prediction outperforms interaction-unaware physics- and map-based approaches.

The 25 Ways AI Can Revolutionize Transportation: From Driverless Trains to Smart Tracks


With massive breakthroughs in smart technologies being reported every month, it won't be long until our transport industries are dominated by AI. Here are just some of the ways artificial intelligence is changing the face of transport, and what we can expect in the near future. Autonomous cars have quickly moved from the realm of sci-fi into reality. Though still in the early stages, these AI-driven vehicles could drastically change how we get from A to B in the near future. From plowing snow to collecting garbage, self-driving trucks could soon be taking over a lot of our dirty work.

Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment Artificial Intelligence

Automated vehicles are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve the Automated vehicles' ability of environment recognition and vehicle control, while the attention paid to decision making is not enough though the decision algorithms so far are very preliminary. Therefore, a framework of the decision-making training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following, is trained within this framework. In addition, theoretical analysis and experiments were conducted on setting reward function for accelerating training using deep reinforcement learning. The results show that on the premise of driving comfort, the efficiency of the trained Automated vehicle increases 7.9% compared to the classical traffic model, intelligent driver model. Later on, on a more complex three-lane section, we trained the integrated model combines both car-following and lane-changing behavior, the average speed further grows 2.4%. It indicates that our framework is effective for Automated vehicle's decision-making learning.

AI And Open Data Show Just How Often Cars Block Bus And Bike Lanes


That lawsuit is currently under appeal, but Bell is undeterred. He's expanded his scope to include bus lanes, which, like bike lanes, often play host to vehicles that are not buses. Buses, Bell says, are a less polarizing issue than bikes. Bus ridership has plummeted in New York and route speeds are slowing to a glacial pace. "We should all be able to get behind the bus issue," Bell tells Fast Company.

Deep Reinforcement Learning for Traffic Light Control in Vehicular Networks Machine Learning

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.

Committee of Infrastructure: Civic Agency and Representation

AAAI Conferences

This article examines bias exhibited through machine vision, over optimization in machine learning, representation for previously unrepresented stakeholders, and agency within the context of a speculative city council meeting. In this paper, I present a project that purposely shows bias in order to reveal how easily machine learning algorithms can problematize a situation. I also have created representatives in the form of Artificial Intelligent systems for both human and non-human communities. I identified a scenario, voting for the removal of traffic lights, as a medium to discuss the topic of bias and representation. This work contributes to the discourses of speculative design and civics in design research.

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning Artificial Intelligence

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.