Abstract

This paper reports on the performance of signalised intersection control using vehicle GPS information compared to fixed-time and inductive loop based control. Traffic congestion forecasts estimate an increase of about 60% in 2030. At present, poor choice of signal timings by isolated intersection controllers cause traffic delays that have enormous negative impacts on the economy and environment. Signal timings can be improved by using vehicles' GPS information to overcome the control action deficit at isolated intersections. This new signal control algorithm is beneficial for traffic engineers and governmental agencies, as traffic flow can be optimised and, hence, fuel consumption and emissions decreased. Under the open European Telecommunication Standards Institute (ETSI) Cooperative Awareness Message (CAM) framework, a traffic responsive GPS based vehicle actuation algorithm (GPS-VA) is proposed. GPS-VA uses position and heading data from vehicle status broadcasts, and inferred velocity information to determine vehicle queue lengths and detect vehicles passing through the intersection. The gathered information is then used to actuate intersection signal timings. Microscopic simulations comparing GPS-VA to fixed-time control and inductive loop based vehicle actuation (Loop-VA) on four urban road networks were performed to see how the proposed GPS-VA algorithm performs compared to existing control strategies. The results show that the GPS-VA is an effective alternative to traditional intersection control strategies, offering delay reductions of up to 50% for connected vehicle fleet penetrations above 30%.

Abstract

The lane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSSs) suffer from weak observation geometry or from the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera, a low-cost inertial measurement unit (IMU), and a single-frequency GNSS receiver. The sensor data fusion is implemented as a tightly coupled Kalman filter that corrects the IMU-based trajectory with GNSS observations while employing European Geostationary Overlay Service correction data. Further, we consider vision-based complementary data that serve as an additional source of information. In contrast to other approaches, the camera is not used to infer the motion of the vehicle, but rather for directly correcting the localization results under the usage of map information. More specifically, the so-called camera-to-map alignment is done by comparing virtual 3D views (candidates) created from projected map data with lane geometry features that are extracted from the camera image. One strength of the proposed solution is its compatibility with state-of-the-art map data, which are publicly available from different sources. We validate the approach on real-world data recorded in The Netherlands and show that it presents a promising and cost-efficient means to support future advanced driver assistance systems.