Carsten Hass

Planning of Optimal Collision Avoidance Trajectories with Timed Elastic Bands

Abstract This paper is concerned with the planning of optimal trajectories for vehicle collision avoidance with a Timed Elastic Band (TEB) framework. The avoidance trajectory is represented by a TEB which is optimized with respect to multiple partially conflicting objectives. The resulting trajectory constitutes the optimal compromise between a mere braking and a lane change maneuver that avoids the collision with the smoothest feasible path. The approach is applicable to general critical traffic situations as the TEB considers the constraints imposed by the vehicle dynamics, road boundaries, static obstacles and moving vehicles.

Driving simulator study on an emergency steering assist.

This contribution is concerned with an emergency steering assist and its evaluation through subject testing in a driving simulator. In an emergency traffic situation where a rear end collision is imminent a swerving maneuver is often too difficult for most drivers. Therefore an assistance system is usefull that supports the driver by steering torque overlay. The paper presents the algorithm used for the assistance. The system was prototypically implemented in a driving simulator and testet with subjects to evaluate the benefits and challenges. The results show that the collision avoidance behaviour of the driver can be improved by the emergency steering assist.

A real-time capable model predictive approach to lateral vehicle guidance

The paper at hand proposes a real-time capable approach to combined trajectory planning and control. One single prediction model is used to plan a feasible trajectory and to perform lateral guidance of the vehicle at the same time. Nonlinear model predictive control (NMPC) methods are applied to solve the optimal control problem, which incorporates environmental constraints leading to a model predictive planning and control approach (MPPC). Experiments are conducted utilizing a rapid prototyping system. The analysis shows the versatile application range of the developed algorithm, such as challenging emergency evasive maneuvers as well as automated steering as an approach to lateral guidance of the vehicle in general.

A Model Predictive Approach to Emergency Maneuvers in Critical Traffic Situations

Advanced Driver Assistance Systems (ADAS) that support drivers during emergency maneuvers have been considered in research and development projects before. It is commonplace that the two problems trajectory planning and vehicle control are solved in two different steps. The paper at hand proposes a real time capable method that solves the planning and control problem in one step. This is achieved by using a scheme similar to model predictive control combined with an environmental model to avoid the necessity of a precalculated reference signal, i.e. a trajectory. The real time capability is achieved by a rough discretization of the inputs, which yields the possibility to predict the plants state trajectory for all possible combinations of discretized input values. Compared to an iterative minimization the computational burden is lower and can be determined deterministically. This contribution contains collision avoidance maneuvers in different traffic situations evaluated in simulation and a fixed base driving simulator.

SIMULATION OF VEHICLES MOVING IN SOFT SOIL

In this contribution problems relating to the non-linear interaction between an elastic tire and soft soil are discussed. A basic approach for solving this problem is described, where the friction force is modeled as a function dependent on the lateral displacement of the tire. The sinking of the tire is not accurately modeled – it is approximated by increasing the friction coefficient both linearly and non-linearly. The behavior exhibited using the approaches described is compared with measured behavior from real tests and the potential of the approach is appraised.

Development of a Mechatronic Adaptive Belt Force Control from the Aspect of Safety and Comfort

Abstract Reversible adaptive safety systems will become much more important with the implementation of an extensive pre crash sensing. With early detection of critical driving situations it will be possible to reduce, for instance, belt slack with the belt pretensioner. Furthermore, the prevention of Out Of Position (OOP) situations will be possible by “pulling“ the passengers out of the danger zones of the airbags with an active belt system. The usual belt force limitation used up until now can be extended in the direction of a passenger specific belt force control. This means pretensioning and limiting the belt force depending on specific passenger data, such as weight. In this paper a MADYMO vehicle model with an occupant and a finite element modelled belt system will be presented. The mechatronic belt system is equipped with an actuator and sensors measuring the current belt force. Using the measured belt force an adaptive command variable can be calculated. The actuator applies the force to the belt system. The simulation results show the reduction in occupant injuries. Furthermore modelling extensions and possible fields of application are shown in this paper.