On Maximizing Lateral Clearance of an Autonomous Vehicle in Urban Environments

IEEE Trans. Intell. Veh.

Sippel

et al. 2023

The choice of the reference point in automated vehicles impacts the vehicle's driving behavior. However, this influence is often not considered for planning and control tasks. To find out where the reference point should be located best, we first consider its position to be ideal if the needed lane width on the left and right side of the planned path is equal when cornering with constant curvature. For constantly curved paths we derive the ideal reference point depending on the curvature, using the kinematics of a slip angle free bicycle model. For non-stationary cornering, we analyze different maneuvers and finally, we select the reference point on the front axle. Utilizing this knowledge, the extent of a forward moving vehicle can be reduced to a point model, which does not require the orientation of the vehicle. This enables a simple and still promising approach for collision checking, where the vehicle's needed space is approximated by only one circle around the reference point. Finally, we analyze the influence of the reference point on a lateral feed-forward controller. Thus, we confirm the previously chosen reference point on the front axle for the equally distributed needed lane width and therefore recommend its use.

Section: B Choice Of Reference Point In the Literaturementioning

confidence: 99%

Ideal Reference Point in Planning and Control for Automated Car-Like Vehicles

Popp

IEEE Trans. Intell. Veh.

Sippel

et al. 2023

“…Using a motion model, the vehicle kinematics can be taken into account as optimization constraints. Common examples are [13,14,15,16]. The advantage of these methods is the (mostly) fast solution in the continuous planning space.…”

Section: A Related Workmentioning

confidence: 99%

“…In the literature of trajectory planning, multiple motion models exist. This can be e. g., a simple model of constant turn rate [26], which considers the position and orientation of the vehicle or a single track model [14,27,28], which represents the non-holonomic motion of the vehicle. While the kinematic single track model assumes slip-free driving, the dynamic single track model [29,30] considers the forces between the tires and the road surface.…”

Section: Motion Modelmentioning

confidence: 99%

Anytime Tree-Based Trajectory Planning for Urban Driving

IEEE Open J. Intell. Transp. Syst.

Adamy

2023

The personal mobility of the future will be changed significantly by autonomous driving. To realize this vision, the complex task of trajectory planning needs to be solved. In this article, a novel planning concept, CarPre trajectory planning, based on Monte-Carlo tree search, is presented. Using a speed-dependent steering angle transformation, the state space of a kinematic single track model is discretized. The planner can then choose between different actions, each consisting of a discrete-value pair of an acceleration and a steering rate. With this, an equitemporal search tree is created to compute the future trajectory. Using Monte-Carlo simulations, the influence of short-term actions of the vehicle can be evaluated over a longer planning horizon. Thus, the current best solution can be accessed at any point during computation, enabling real-time applications. Furthermore, the discretized search tree enables easy checking of complex constraints dependent on binary or continuous variables. The concept is verified on a real test vehicle in a lane keeping maneuver. Through initial testing, a pleasant driving experience is perceived, which indicates future acceptance of the real-time capable algorithm.INDEX TERMS Anytime, automated vehicles, MCTS, motion planning, real-time, test vehicle, trajectory planning, urban driving

“…The kinematic bicycle model [22] is a simple but accurate vehicle model which describes the non-holonomic movements of a car-like, front-steered vehicle. It is well established in the trajectory planning community and is a common choice e. g. when planning with model predictive control [23]. Furthermore, Polack et al [24] presented that by limiting the lateral acceleration to 0.54µg, the kinematic bicycle model approximates the movement of a real vehicle sufficiently.…”

Section: Kinematic Bicycle Modelmentioning

confidence: 99%

Modeling Driving Behavior of Human Drivers for Trajectory Planning

IEEE Trans. Intell. Transport. Syst.

Willert

Adamy

2022

Extracted driving behavior of human driven vehicles can benefit the development of various applications like trajectory prediction or planning, abnormal driving detection, driving behavior classification, traffic simulation modeling, etc. In this paper, we focus on modeling human driving behavior in order to find simplifications for trajectory planning. Using a time-discrete kinematic bicycle model with the vehicle's acceleration and steering rate as inputs, we model the human driven trajectories of an urban intersection drone dataset for different input sampling times. While most planning algorithms are using input sampling times below 0.33 s, we are able to model 98.2 % of the human driven trajectories of the investigated dataset with a sampling time of 0.6 s. Using longer input sampling times can result in smoother trajectories and longer planning horizons, and thus more efficient trajectories. In a next step, we analyze the correlations between the input of our model and the current state/last input. Such a priori knowledge could simplify common planning algorithms like model predictive control or tree-search based planners by limiting the action space of the ego-vehicle. We propose nonlinear transformations for steering rate and steering angle to represent correlations between speed, acceleration, steering angle and steering rate. In the transformed space the statistics are very well modeled by multivariate Gaussian distributions. Using a multivariate Gaussian, a fast usable behavior model is extracted which is independent of the environment.