Manual optimization of traffic light cycles is a complex and time-consuming task, necessitating the development of automated solutions. In this paper, we propose the application of reinforcement learning to optimize traffic light cycles in real-time. We present a case study using the Simulation Urban Mobility simulator to train a Deep Q-Network algorithm. The experimental results showed 44.16% decrease in the average number of Emergency stops, showing the potential of our approach to reduce traffic congestion and improve traffic flow. Furthermore, we discuss avenues for future research and enhancements to the reinforcement learning model.

Automated driving systems can be helpful in a wide range of societal challenges, e.g., mobility-on-demand and transportation logistics for last-mile delivery, by aiding the vehicle driver or taking over the responsibility for the dynamic driving task partially or completely. Ensuring the safety of automated driving systems is no trivial task, even more so for those systems of SAE Level 3 or above. To achieve this, mechanisms are needed that can continuously monitor the system's operating conditions, also denoted as the system's operational design domain. This paper presents a safety concept for automated driving systems which uses a combination of onboard runtime monitoring via connected dependability cage and off-board runtime monitoring via a remote command control center, to continuously monitor the system's ODD. On one side, the connected dependability cage fulfills a double functionality: (1) to monitor continuously the operational design domain of the automated driving system, and (2) to transfer the responsibility in a smooth and safe manner between the automated driving system and the off-board remote safety driver, who is present in the remote command control center. On the other side, the remote command control center enables the remote safety driver the monitoring and takeover of the vehicle's control. We evaluate our safety concept for automated driving systems in a lab environment and on a test field track and report on results and lessons learned.

Online trajectory planners enable quadrotors to safely and smoothly navigate in unknown cluttered environments. However, tuning parameters is challenging since modern planners have become too complex to mathematically model and predict their interaction with unstructured environments. This work takes humans out of the loop by proposing a planner parameter adaptation framework that formulates objectives into two complementary categories and optimizes them asynchronously. Objectives evaluated with and without trajectory execution are optimized using Bayesian Optimization (BayesOpt) and Particle Swarm Optimization (PSO), respectively. By combining two kinds of objectives, the total convergence rate of the black-box optimization is accelerated while the dimension of optimized parameters can be increased. Benchmark comparisons demonstrate its superior performance over other strategies. Tests with changing obstacle densities validate its real-time environment adaption, which is difficult for prior manual tuning. Real-world flights with different drone platforms, environments, and planners show the proposed framework's scalability and effectiveness.

Much self-driving-car research focuses on pedestrian safety, but it is important to consider passenger safety and comfort, too. When braking to avoid a collision, for example, a vehicle should ideally ease, not slam, into a stop. In machine-learning parlance, this idea constitutes a multi-objective problem. Objective one: spare the pedestrian. Researchers at Ryerson University in Toronto took on this challenge with deep reinforcement learning.

Much self-driving-car research focuses on pedestrian safety, but it is important to consider passenger safety and comfort, too. When braking to avoid a collision, for example, a vehicle should ideally ease, not slam, into a stop. In machine-learning parlance, this idea constitutes a multi-objective problem. Objective one: spare the pedestrian. Researchers at Ryerson University in Toronto took on this challenge with deep reinforcement learning.

Volvo is to install technology in its self-driving cars that can detect if the driver is drunk. The Swedish carmaker said that from next year all new vehicles will have cameras and sensors to spot if the motorist is showing signs of being over the limit. Cars will slow down by before ringing the Volvo call centre where a member of the customer service will speak to the driver and take over the car if necessary. The self-driving vehicle may even park the car by itself if the driver is unresponsive. The new safety features are part of the manufacturers pledge to eliminate all passenger deaths.

Back in March, an Uber self-driving car killed 49-year-old Elain Herzberg in Tempe, Arizona, after failing to do an emergency stop. After a US federal investigation, it is thought that the car did not stop because the system put in place to carry out emergency stops in dangerous situations was disabled . National Transportation Safety Board officials inspecting the car that killed Mrs Herzberg. So, how do self-driving cars actually work? Most self-driving cars have a GPS unit, a range of sensors such as radar, video and laser rangefinders as well as a navigation system.

When Microsoft showed off Minecraft for the Oculus Rift last fall, the experience was quite realistic--and so was the nausea. On Monday, Microsoft said it's begun shipping the VR version of Minecraft to everyone, complete with two important tweaks that should help reduce VR vertigo. Microsoft began shipping a free update to Minecraft: Windows 10 Edition Beta on Monday that added VR support for the Oculus Rift. It's actually the second VR-specific update Microsoft and developer Mojang have released for Minecraft, following the Minecraft for Gear VR edition the company released in May. "Simulator sickness," one of the names given to the sort of nausea that can accompany virtual reality software, depends on a number of factors, all of which boil down to convincing your brain that what you're seeing is actually "real," rather than an illusion that should be dispelled.