Blaine Levedahl

Model predictive driver assistance control for cooperative cruise based on hybrid system driver model

This paper presents a driver assisting system for cooperative cruising of multiple cars. In order to account for personal difference of individual drivers, each drivers vehicle following skill on expressways is identified as a PrARX model, a continuous approximation of hybrid dynamical system. The PrARX model describes the drivers logical decision making as well as continuous maneuvering in a uniform manner. The assisting acceleration is computed in the framework of model predictive control, where the plant model is a platoon of cars coupled with PrARX driver models. For computing assisting outputs in real time, a fast computation method for nonlinear model predictive control based on the continuation technique is employed. The proposed assisting system is tested in numerical simulations and on a driving simulator with a real human driver. The high speed calculation of the Homotopy method is also proved by comparison to the conventional method. Finally, the advantage of global optimization for cooperative safety is confirmed by comparing its control performance with the local optimization for individual safety.

Trajectory planning for automated parking using multi-resolution state roadmap considering non-holonomic constraints

This paper presents a trajectory planning method for automated parking. The proposed method constructs a state roadmap in which each node contains not only position but also orientation information of the vehicle. The roadmap is constructed by dividing the orientation space in multiple resolutions considering the non-holonomic constraints of the vehicle and the collision-avoidance constraints between the vehicle and the boundary of the parking environment. Using the state roadmap, a complex parking trajectory composed of both forward and reverse motions can be computed with small online computation cost. The proposed method is evaluated in both numerical simulations and an experiment using an electric vehicle.

Variable-resolution velocity roadmap generation considering safety constraints for mobile robots

This research develops a new roadmap method for autonomous mobile robots based on variable-resolution partitioning of a continuous state space. Unlike conventional roadmaps, which include position information only, the proposed roadmap also includes velocity information. Each node of the proposed roadmap consists of a fixed position and a range of velocity values, where the velocity ranges are determined by variable-resolution partitioning of the velocity space. An ordered pair of nodes is connected by a directed link if any combination of their velocity values is within the acceptable range of the nodes and produces a trajectory satisfying a set of safety constraints. In this manner, a possible trajectory connecting an arbitrary starting node and destination node is obtained by applying a graph search technique on the proposed roadmap. The proposed method is evaluated through simulations.

Variable-resolution velocity-time roadmap generation considering safety constraints for autonomous vehicles

This research develops a new roadmap method for autonomous vehicles based on variable-resolution partitioning of a continuous state-time space. Unlike conventional roadmaps, which include position information only, the proposed roadmap also includes velocity and time information. Each node of the proposed roadmap contains position and velocity information and is the same as a Variable-resolution Velocity Roadmap (VVR). Each directed link of the proposed roadmap has a range of time values, where the time ranges are determined by variable-resolution partitioning of the time space. An ordered pair of nodes is connected by a directed link within a time region if any combination of their velocity and time values is within the acceptable range of the nodes and produces a trajectory satisfying a set of safety constraints. In this manner, a possible trajectory, with transit time, connecting an arbitrary starting node and destination node is obtained by applying a graph search technique on the proposed roadmap. The proposed method is demonstrated through simulations.

Variable-resolution state roadmap generation considering safety constraints for car-like robot

This research develops a new graph-map method for autonomous car-like mobile robots based on variable-resolution division of space. Unlike conventional roadmaps, which include position information only, the proposed graph-map also includes orientation information of the car-like robot. In this manner, the robot is able to plan a path in detail. The orientation information of each node is not a fixed value but a range. The range is constructed by dividing the orientation space using variable-resolution, which is obtained from the surrounding situation of links that connect to the node. Finally, the proposed method is evaluated through simulations.

Computation of energy-optimal velocity profile for electric vehicle considering slope of route

This paper presents a computation method of a speed profile which minimizes energy loss considering an identified energy loss characteristics and slope inclination of the road. At first, the real driving data are observed to measure the energy consumption and the energy loss, and the energy loss model of target vehicle is identified from measured information. Secondly slope of the driving path is also expressed as a function of the location. Then, the optimization problem is formulated as a non-linear programming problem [11] including identified energy loss model and slope function together with a simple vehicle model. Then the speed profiles which minimizes the energy loss is obtained by solving a formulated optimization problem. Finally the energy efficiency of the obtained speed profile is confirmed.

Energy Consumption Evaluation Based on a Personalized Driver–Vehicle Model

A new approach to evaluate personalized energy consumption is presented in this paper. The method consists of identifying driver–vehicle dynamics using the probability weighted autoregressive model, which is one of the multi-mode ARX models, and then of reproducing the driver–vehicle behavior in a vehicle-following task. The energy consumption of the vehicle is estimated from the velocity profile calculated by using the driver–vehicle model. In this paper, driving simulator and real-world driving data were recorded to identify the driver–vehicle model in various situations. As a result, real-world energy consumption could be reproduced in a variety of situations with an average error of 1.9% and a standard deviation within 1.5%. Several promising applications of the energy consumption evaluation are introduced in this paper, such as an online energy consumption prediction, a powertrain choice-assistance system for car buyers, and a solution to estimate the macroscopic energy consumption of aggregated vehicles in a traffic flow.

Quantitative Evaluation of Distracted Driving by Using a PrARX Model

This research develops a metric for the evaluation of an automobile drivers distraction based on a mathematical driving behavior model. Driving data was collected in a driving simulator. The primary task was to maintain a constant following distance behind a lead vehicle. The secondary task, which brings about the distraction, is to operate the in-car touch panel. A PrARX model is used to describe the vehicle-following behavior. In the PrARX model, the weighting parameter represents the drivers logical decision making and the auto-regressive exogenous models characterize the drivers continuous-time motion control behavior. By calculating the entropy of the PrARX model, the drivers distraction, which is considered a degradation of decision-making ability, is assessed in a quantitative manner.