Ryo Yanase

Mono-camera based vehicle localization using lidar intensity map for automated driving

This paper reports an image-based localization for automated vehicle. The proposed method utilizes a mono-camera and an inertial measurement unit to estimate the vehicle pose. Self-localization is implemented by a map matching technique between the reference digital map and sensor observations. In general, the same types of sensors are used for map data and observations. However, this study is focused on the mono-camera based method using Lidar-based map for the purpose of a low-cost implementation. Image template matching is applied to provide a correlation distribution between the captured image and the predefined orthogonal map. A probability of the vehicle pose is then updated using the obtained correlation. The experiments were carried out for real driving data on an urban road. The results have verified that the proposed method estimates the vehicle position in 0.11[m] positioning errors on real-time.

Trajectory optimization and state selection for urban automated driving

The automated driving is an emerging technology in which a car performs recognition, decision making, and control. The decision-making system consists of route planning and trajectory planning. The route planning optimizes the shortest path to the destination like an automotive navigation system. According to static and dynamic obstacles around the vehicle, the trajectory planning generates lateral and longitudinal profiles for vehicle maneuver to drive the given path. This study is focused on the trajectory planning for vehicle maneuver in urban traffic scenes. This paper proposes a trajectory generation method that extends the existing method to generate more natural behavior with small acceleration and deceleration. This paper introduces an intermediate behavior to gradually switch from the velocity keeping to the distance keeping. The proposed method can generate smooth trajectory with small acceleration/deceleration. Numerical experiments show that the vehicle generates smooth behaviors according to surrounding vehicles.

Abstraction refinement for non-zeno fairness verification of linear hybrid automata

Linear hybrid automaton is a specification language for hybrid systems. For verification of hybrid systems, it is important to check fairness assumptions. For example, an embedded system keeps running forever when it starts to move by turning on the switch. Such a system has to be checked not only system safety but also fairness and non-Zenoness. The state space explosion is a fundamental problem in model checking, since it is a method that performs an exhaustive search of states. To avoid state space explosion problem in model checking, CEGAR (Counter Example Guided Abstraction Re?nement) is an effective technique. In this paper, we propose transition predicate abstraction and CEGAR verification algorithm for linear hybrid automata.

Supervised Calibration Method for Improving Contrast and Intensity of LIDAR Laser Beams

Calibration of LIDAR laser beams in terms of contrast and intensity levels is very important for map generation and localization of autonomous vehicles. In this paper, we explain a simple semi-calibration method based on matching the shape and distribution of histograms. A laser beam output is selected to be a reference of the calibration process after manually tuning its intensity and contrast parameters to describe the road marks in prominent reflectivity. The histograms of the other laser beams are then aligned to the reference histogram and the calibration parameters of each beam are obtained. The experimental results have verified that the proposed method is reliable and provides a considerable enhancement of the map image quality as well as it improves the localization accuracy during the autonomous driving.

A Case Study of Formal Approach to Dynamically Reconfigurable Systems by Using Dynamic Linear Hybrid Automata

Networking systems and embedded systems are able to change their configuration, components and modules at run-time. Such a system is called dynamically reconfigurable system. For guaranteeing safety of the system, model checking is one of the effective methods. This paper presents a dynamic linear hybrid automaton (DLHA) as a specification language for designing dynamically reconfigurable systems. As a practical experiment, we describe an embedded cooperative system consisting of CPU and DRP by DLHAs and verify several properties for the system with a model checker that performs the reachability analysis by using monitor automata.

Formal verification of dynamically reconfigurable systems

A dynamically reconfigurable system can perform complicated operations with dynamically changing the configuration. For ensuring the safety of the system, a model checking is one of the efficient formal approach. In our work, we define the specification language of a dynamically reconfigurable system and propose the model checking algorithm of verifying safety properties.

Development of Model Checker of Dynamic Linear Hybrid Automata

Dynamically reconfigurable systems have attracted public attention from the point of view of miniaturization and saving power consumption for embedded systems in recent years. In this study, we propose dynamic linear hybrid automata as specification language of dynamically reconfigurable systems and the verification technique of reachability analysis. A dynamic linear hybrid automaton(DLHA) is a linear hybrid automaton extended with actions of creation and destruction. This paper presents the model checker and applies it to the model of an embedded system consisting CPU and DRP.