Indoor intelligent driving scene-based traffic signal data set

Signal strength based indoor geolocation

∗ This research has been supported, in part, by grants from NTT DoCoMo, Inc., NJCWT (the New Jersey Center for Wireless Telecommunications), Panasonic Technologies, Inc., and Project 69931040 supported by NSFC. Abstract We have investigated the indoor geolocation based on signal strength modeling. Linear, compensated linear, and multiple regression methods have been applied to set up signal str...

Intelligent Entropy-based Traffic Control

Stixmentation - Probabilistic Stixel based Traffic Scene Labeling

The detection of moving objects like vehicles, pedestrians or bicycles from a mobile platform is one of the most challenging and most important tasks for driver assistance and safety systems. For this purpose, we present a multi-class traffic scene segmentation approach based on the Dynamic Stixel World, an efficient super-pixel object representation. In this approach, each Stixel is assigned e...

Segmentation-Based Urban Traffic Scene Understanding

We propose a method to recognize the traffic scene in front of a moving vehicle with respect to the road topology and the existence of objects. To this end, we use a two-stage system, where the first stage abstracts from the underlying image by means of a rough super-pixel segmentation of the scene. In a second stage, this meta representation is then used to construct a feature set for a classi...

Robust Optimal Eco-driving Control with Uncertain Traffic Signal Timing

This paper proposes a robust optimal eco-driving control strategy considering multiple signalized intersections with uncertain traffic signal timing. A spatial vehicle velocity profile optimization formulation is developed to minimize the global fuel consumption, with driving time as one state variable. We introduce the concept of effective red-light duration (ERD), formulated as a random varia...