Andreas Sasse

Learning Traffic Light Phase Schedules from Velocity Profiles in the Cloud

Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the traffic light phase schedule, they could predict the traffic light state at arrival time and could reduce fuel consumption. To acquire information like traffic light phase schedules, our vision is that drivers share their velocity profiles in a digital cloud, and in return benefit from smart algorithms evaluating the collected data. We present one such algorithm, Traffic Light State Estimation (TLSE), that operates on the velocity profiles to backward-estimate phase schedules of traffic light signal groups operating with fixed cycle length (representing about 80% of all traffic lights in the US). We present simulation results showing that phase schedule prediction on the base of TLSE is correct more than 90% of the time.

CQI Maps for Optimized Data Distribution

The number of connected cars is growing continuously. Many car manufacturers offer systems, which provide online content to the passengers, mostly infotainment and information data. Although new wireless data communications technologies like LTE will offer much more performance, the increasing number of mobile data users in combination with higher individual data traffic will nullify the higher capacities. Mobile data bandwidth will remain a rare commodity. If next generations driver assistance systems should be supplied with data from central servers in time and according to their individual priorities, it is important to make optimal use of the available resources. Considering the changing channel conditions of the wireless links offers potential for improvements, since the channel quality has a direct influence on the utilization of the data channel. HSPA and LTE use the so called Channel Quality Indicator (CQI) as a measure of the channel conditions. Summarized in a map, server-side applications can use a priori knowledge of the location-dependent channel quality to optimize the data distribution to mobile clients. This paper describes the process of creating CQI maps for HSDPA networks, from the initial collection of raw data to the final validation of the proposed concept.