Krister Wolff

A Review of Near-Collision Driver Behavior Models

Objective: This article provides a review of recent models of driver behavior in on-road collision situations. Background: In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments. Method: Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios. Results: The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms. Conclusion: A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended. Application: This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work.

Learning biped locomotion from first principles on a simulated humanoid robot using linear genetic programming

We describe the first instance of an approach for control programming of humanoid robots, based on evolution as the main adaptation mechanism. In an attempt to overcome some of the difficulties with evolution on real hardware, we use a physically realistic simulation of the robot. The essential idea in this concept is to evolve control programs from first principles on a simulated robot, transfer the resulting programs to the real robot and continue to evolve on the robot. The Genetic Programming system is implemented as a Virtual Register Machine, with 12 internal work registers and 12 external registers for I/O operations. The individual representation scheme is a linear genome, and the selection method is a steady state tournament algorithm. Evolution created controller programs that made the simulated robot produce forward locomotion behavior. An application of this system with two phases of evolution could be for robots working in hazardous environments, or in applications with remote presence robots.

Structural Evolution of Central Pattern Generators for Bipedal Walking in 3D Simulation

Anthropomorphic walking for a simulated bipedal robot has been realized by means of artificial evolution of central pattern generator (CPG) networks. The approach has been investigated through full rigid-body dynamics simulations in 3D of a bipedal robot with 14 degrees of freedom. The half-center CPG model has been used as an oscillator unit, with interconnection paths between oscillators undergoing structural modifications using a genetic algorithm. In addition, the connection weights in a feedback network of predefined structure were evolved. Furthermore, a supporting structure was added to the robot in order to guide the evolutionary process towards natural, human-like gaits. Subsequently, this structure was removed, and the ability of the best evolved controller to generate a bipedal gait without the help of the supporting structure was verified. Stable, natural gait patterns were obtained, with a maximum walking speed of around 0.9 m/s.

Evolving 3D model interpretation of images using graphics hardware

We present a novel approach for 3D-scene interpretation with numerous applications, for instance in robotics. The models are rendered using 3d graphics hardware and DirectX. Both artificial and real images were used to test the system. More than one target image can be used, allowing stereoscopic vision. These experiments present results of interesting generalization.

Effects of experience and electronic stability control on low friction collision avoidance in a truck driving simulator

Two experiments were carried out in a moving-base simulator, in which truck drivers of varying experience levels encountered a rear-end collision scenario on a low-friction road surface, with and without an electronic stability control (ESC) system. In the first experiment, the drivers experienced one instance of the rear-end scenario unexpectedly, and then several instances of a version of the scenario adapted for repeated collision avoidance. In the second experiment, the unexpected rear-end scenario concluded a stretch of driving otherwise unrelated to the study presented here. Across both experiments, novice drivers were found to collide more often than experienced drivers in the unexpected scenario. This result was found to be attributable mainly to longer steering reaction times of the novice drivers, possibly caused by lower expectancy for steering avoidance. The paradigm for repeated collision avoidance was able to reproduce the type of steering avoidance situation for which critical losses of control were observed in the unexpected scenario and, here, ESC was found to reliably reduce skidding and control loss. However, it remains unclear to what extent the results regarding ESC benefits in repeated avoidance are generalisable to unexpected situations. The approach of collecting data by appending one unexpected scenario to the end of an otherwise unrelated experiment was found useful, albeit with some caveats.

Evolutionary optimization of a bipedal gait in a physical robot

Evolutionary optimization of a gait for a bipedal robot has been studied, combining structural and parametric modifications of the system responsible for generating the gait. The experiment was conducted using a small 17 DOF humanoid robot, whose actuators consist of 17 servo motors. In the approach presented here, individuals representing a gait consisted of a sequence of set angles (referred to as states) for the servo motors, as well as ramping times for the transition between states. A hand-coded gait was used as starting point for the optimization procedure: A population of 30 individuals was formed, using the hand-coded gait as a seed. An evolutionary procedure was executed for 30 generations, evaluating individuals on the physical robot. New individuals were generated using mutation only. There were two different mutation operators, namely (1) parametric mutations modifying the actual values of a given state, and (2) structural mutations inserting a new state between two consecutive states in an individual. The best evolved individual showed an improvement in walking speed of approximately 65%.

A Brute-Force Approach to Automatic Induction of Machine Code on CISC Architectures

The usual approach to address the brittleness of machine code in evolution is to constrain mutation and crossover to ensure syntactic closure. In the novel approach presented here we use no constraints on the operators. They all work blindly on the binaries in memory but we instead encapsulate the code and trap all resultingexceptions using the built-in error reportingmec hanisms which modern CPUs provide to the operatingsystem. Thus it is possible to return to very simple genetic operators with the objective of increased performance. Furthermore the instruction set used by evolved programmes is no longer limited by the genetic programming system but only by the CPU it runs on. The mappingb etween the evolution platform and the execution platform becomes almost complete, ensuringcorrect low-level behaviour of all CPU functions.

A simulation environment for analysis and optimization of driver models

A simulation environment for evaluation and optimization of driver models is introduced and described. The simulation environment features models of vehicles and drivers, as well as a representation of the traffic environment (roads, buildings etc.). In addition, an optimization framework based on stochastic optimization algorithms has been implemented as an integral part of the simulation environment. Given observed (time series) data of driver behavior and, possibly, vehicle dynamics, the optimization framework can be used for inferring driver model parameters. The simulation environment has been evaluated in two scenarios, one involving emergency braking and one involving a double lane change.

Central Pattern Generators for Gait Generation in Bipedal Robots

An obvious problem confronting humanoid robotics is the generation of stable and efficient gaits. Whereas wheeled robots normally are statically balanced and remain upright regardless of the torques applied to the wheels, a bipedal robot must be actively balanced, particularly if it is to execute a human-like, dynamic gait. The success of gait generation methods based on classical control theory, such as the zero-moment point (ZMP) method (Takanishi et al., 1985), relies on the calculation of reference trajectories for the robot to follow. In the ZMP method, control torques are generated in order to keep the zero-moment point within the convex hull of the support area defined by the feet. When the robot is moving in a well-known environment, the ZMP method certainly works well. However, when the robot finds itself in a dynamically changing real-world environment, it will encounter unexpected situations that cannot be accounted for in advance. Hence, reference trajectories can rarely be specified under such circumstances. In order to address this problem, alternative, biologically inspired control methods have been proposed, which do not require the specification of reference trajectories. The aim of this chapter is to describe one such method, based on central pattern generators (CPGs), for control of bipedal robots. Clearly, walking is a rhythmic phenomenon, and many biological organisms are indeed equipped with CPGs, i.e. neural circuits capable of producing oscillatory output given tonic (non-oscillating) activation (Grillner, 1996). There exists biological evidence for the presence of central pattern generators in both lower and higher animals. The lamprey, which is one of the earliest and simplest vertebrate animals, swims by propagating an undulation along its body. The wave-like motion is produced by an alternating activation of motor neurons on the left and right sides of the segments along the body. The lamprey has a brain stem and spinal cord with all basic vertebrate features, but with orders of magnitude fewer nerve cells of each type than higher vertebrates. Therefore, it has served as a prototype organism for the detailed analysis of the nervous system, including CPGs, in neurophysiological studies (Grillner, 1991; Grillner, 1995). In some early experiments by Brown (Brown, 1911, Brown, 1912), it was shown that cats with transected spinal cord and with cut dorsal roots still showed rhythmic alternating contractions in ankle flexors and extensors. This was the basis of the concept of a spinal locomotor center, which Brown termed the half-center model (Brown, 1914). Further biological support for the existence of a spinal CPG structure in vertebrates is presented in (Duysens & Van de Crommert, 1998).

Evolution of Biped Locomotion Using Linear Genetic Programming

Gait generation for bipedal robots is a very complex problem. The basic cycle of a bipedal gait, called a stride, consists of two main phases, namely the single-support phase and the double-support phase, which take place in sequence. During the single-support phase, one foot is in contact with the ground and the other foot is in swing motion, being transferred from back to front position. In the double-support phase, both feet simultaneously touch the ground, and the weight of the robot is shifted from one foot to the other. During the completion of a stride, the stability of the robot changes dynamically, and there is always a risk of tipping over. Thus it is crucial to actively maintain the stability and walking balance of the robot at all times. In the conventional engineering approach, there are two main methods for bipedal gait synthesis: Off-line trajectory generation, and on-line motion planning (Wahde and Pettersson, 2002; Katic and Vukobratovic, 2003). Both these methods rely on the calculation of reference trajectories, such as e.g. trajectories of joint angles, for the robot to follow. An off-line controller assumes that there exists an adequate dynamic model of the robot and its environment, which can be used to derive a body motion that adheres to a stability criterion, such as e.g. the zero-moment point (ZMP) criterion (Li et al., 1992; Huang et al., 2001; Huang and Nakamura, 2005; Hirai et al., 1998; Yamaguchi et al., 1999; Takanishi et al., 1985) that requires the ZMP to stay within an allowable region, namely the convex hull of the support region defined by the feet. An on-line motion controller, on the other hand, uses limited knowledge of the kinematics and dynamics of the robot and its environment (Furusho and Sano, 1990; Fujimoto et al., 1998; Kajita and Tani, 1996; Park and Cho, 2000; Zheng and Shen, 1990). Instead, simplified models are used to describe the relationship between input and output. This method also relies much on real-time feedback information. Control policies based on classical control theory, like the ones outlined above, have been successfully implemented on bipedal robots in a number of cases, see e.g. the references mentioned in the previous paragraph. When the robot is operating in a well-known, structured environment, the abovementioned control methods normally work well. However, the success of these methods relies on the calculation of reference trajectories for the robot to follow. When the robot is moving in a realistic, dynamically changing environment such reference trajectories can rarely be specified, since the events that might