Moritz Werling

Towards fully autonomous driving: Systems and algorithms

In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential.

Automatic collision avoidance using model-predictive online optimization

In many traffic emergency situations a collision cannot be prevented by braking alone. Therefore, we propose an obstacle avoidance algorithm that simultaneously optimizes steering and braking. As an emergency scenario approaches the driving limits, a strong nonlinear constraint between braking and cornering develops, suggesting the formulation of a nonlinear constrained online optimization. On this account the proposed algorithm embarks on the nonlinear model-predictive control (NMPC) paradigm, which is capable of solving the optimization problem online. The performance of the algorithm is demonstrated in a simulated pedestrian collision avoidance scenario.

Reversing the General One-Trailer System: Asymptotic Curvature Stabilization and Path Tracking

Backing up a trailer can be a challenge, particularly for inexperienced recreational drivers. We therefore develop two feedback controllers, which support the driver with automatic steering inputs in various situations. Based on the kinematics of the general one-trailer system, we first derive an input/output-linearizing control law that asymptotically stabilizes a given curvature for the trailer. This enables the driver to directly steer the trailer, e.g., by means of a turning knob, such that the trailer will automatically be prevented from jackknifing. The control task is then modified and solved so that the vehicle can also take over the complete stabilization task along given paths. In combination with a path-planning algorithm, this enables automated parallel parking for example. The complete system is implemented on a rapid-prototyping environment and evaluated in real-world scenarios.

Lane-based safety assessment of road scenes using Inevitable Collision States

This paper presents a method for reasoning about the safety of traffic situations. More precisely, the problem of safety assessment for partial trajectories for vehicles is addressed. Therefore, the Inevitable Collision States (ICS) as well as its probabilistic generalization the Probabilistic Collision States (PCS) are used. Thereby, the assessment is performed for an infinite time horizon. For solving the ICS computation nonlinear programming is applied. In addition to the safety assessment an evaluation of the disturbance of the other traffic participants by the ego vehicle is presented. The results are integrated into an optimal control based planning approach that generates minimum jerk trajectories. An example implementation of the proposed framework is applied to simulation scenarios that demonstrates the necessity of the presented method for guaranteeing motion safety.

On-line Trajectory Generation for Safe and Optimal Vehicle Motion Planning

This paper presents a framework for motion planning of autonomous vehicles, it is characterized by its efficient computation and its safety guarantees. An optimal control based approach generates comfortable and physically feasible maneuvers of the vehicle. Therefore, a combined optimization of the lateral and longitudinal movements in street-relative coordinates with carefully chosen cost functionals and terminal state sets is performed. The collision checking of the trajectories during the planning horizon is also performed in street-relative coordinates. It provides continuous collision checking, which covers nearly all situations based on an algebraic solution and has a constant response time. Finally, the problem of safety assessment for partial trajectories beyond the planning horizon is addressed. Therefore, the Inevitable Collision States (ICS) are used, extending the safety assessment to an infinite time horizon. To solve the ICS computation nonlinear programming is applied. An example implementation of the proposed framework is applied to simulation scenarios that demonstrates its efficiency and safety capabilities.

Maneuver prediction at intersections using cost-to-go gradients

According to the analysis of car accidents many casualties occur at intersections. As ongoing research demonstrates, Advanced Driver Assistance Systems that aim at preventing this type of accident, need to reliably predict the turning maneuver of all relevant participants in the scene. In this work an approach is introduced, which models human drivers as the optimizer of an optimal control problem with an unknown terminal state. Tracking the cost-to-go gradient to the terminal state of each driving option leads to the most plausible hypothesis. The optimal control problem itself is formulated with costs that minimize jerk, time and steering effort with good resemblance to typical human driving behavior. In combination with a simplified vehicle model this leads to a nonlinear constrained dynamic optimization problem, which is solved numerically. The performance of the proposed approach is evaluated on data obtained in a field test with promising results.

Lateral Vehicle Trajectory Optimization Using Constrained Linear Time-Varying MPC

In this paper, a trajectory optimization algorithm is proposed, which formulates the lateral vehicle guidance task along a reference curve as a constrained optimal control problem. The optimization problem is solved by means of a linear time-varying model predictive control scheme that generates trajectories for path following under consideration of various time-varying system constraints in a receding horizon fashion. Formulating the system dynamics linearly in combination with a quadratic cost function has two great advantages. First, the system constraints can be set up not only to achieve collision avoidance with both static and dynamic obstacles, but also aspects of human driving behavior can be considered. Second, the optimization problem can be solved very efficiently, such that the algorithm can be run with little computational effort. In addition, due to an elaborate problem formulation, reference curves with discontinuous, high curvatures will be effortlessly smoothed out by the algorithm. This makes the proposed algorithm applicable to different traffic scenarios, such as parking or highway driving. Experimental results are presented for different real-world scenarios to demonstrate the algorithm’s abilities.

Automatic collision avoidance during parking and maneuvering — An optimal control approach

In order to reduce the great number of parking incidences and other collisions in low speed scenarios, an obstacle avoidance algorithm is proposed. Since collision avoidance can be achieved by sole braking when driving slowly this algorithms objective is a comfort orientated braking routine. Therefore, an optimization problem is formulated leading to jerk and time optimal trajectories for braking. In addition to that, based on the prediction of the future vehicle motion, this algorithm compensates for a significant actuator time delay. Using an occupancy grid for representing the static vehicle environment and the current driving state, possible collision points are determined not only for the vehicle front or rear, but also for both vehicle sides, where no sensors are located. The algorithms performance is demonstrated in a real-world scenario.

Estimation of collective maneuvers through cooperative multi-agent planning

In order to determine a cooperative driving strategy, it is beneficial for an autonomous vehicle to incorporate the intended motion of surrounding vehicles within its own motion planning. However, as intentions cannot be measured directly and the motion of multiple vehicles often are highly interdependent, this incorporation has proven challenging. In this paper, the problem of maneuver estimation is addressed, focusing on situations with close interaction between traffic participants. Therefore, we define collective maneuvers based on trajectory homotopy, describing the relative motion of multiple vehicles in a scene. Representing maneuvers by sample trajectories, maneuver-dependent prediction models of the vehicle states can be defined. This allows for a Bayesian estimation of maneuver probabilities given observations of the real motion. The approach is evaluated by simulation in overtaking scenarios with oncoming traffic and merging scenarios at an intersection.