Automated driving

Predicting of Machine Perception for Automated Driving

In the case of highly-automated and fully-automated driving it is necessary for the vehicle itself to recognize the limitations of its machine perception, as well as the functional limitations of processing modules based on this perception to react adequately. While simulator studies of highly-automated driving have shown that realistic transfer times to the driver of between 5 and 10 s can be ...

Architecting Safe Automated Driving with Legacy Platforms

Modern vehicles have electrical architectures whose complexity grows year after year due to feature growth corresponding to customer expectations. The latest of the expectations, automation of the dynamic driving task however, is poised to bring about some of the largest changes seen so far. In one fell swoop, not only does required functionality for automated driving drastically increase the s...

European Roadmap Smart Systems for Automated Driving

Policy Gradient Methods for Automated Driving

Automated driving requires designing a system capable of maintaining safety while simultaneously maintaining passenger comfort. Models of highway driving have high dimensionality and stochasticity traditionally specifying the histories for a large, varying number of agents in a continuous state and action space. Traditional value-based reinforcement learning methods require exponential time in ...

Automated Drowsiness Detection For Improved Driving Safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous...