Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios

Situation-based Risk Evaluation and Behavior Planning

The presented dissertation addresses the problem of risk evaluation and behavior planning for future intelligent Advanced Driver Assistance Systems (ADAS). For this purpose, a novel framework for situation-based risk evaluation and behavior planning, targeting highly automated driving, is presented. After properly sensing the current scene, including the current road topology and other traffic participants, the proposed framework first estimates and predicts the future behavior of all involved entities comprising a situation classification and trajectory prediction step. This is then followed by the generation of the own future behavior in a behavior planning step which is based on an evaluation of possible ego behavior alternatives in terms of risk and utility considerations. The future behavior is planned in a way to find a tradeoff between the expected future risk and utility. Inner-city traffic scenarios in particular are usually complex and of high uncertainty, considering measurements as well as behavioral decisions. To reduce the complexity, similar behavior alternatives are clustered and represented by prototypical behavior patterns using so-called situations. A novel situation classification approach is proposed to estimate how good a situation matches with the actual behaviors. This approach is based on a comparison of the prototypically predicted trajectories of the considered situations with the actual measured trajectories. For this purpose a novel measure for spatio-temporal trajectory similarity, based on the evaluation of longitudinal and lateral spatio-temporal distance, is derived. The situation classification system is used to detect incorrect and critical traffic behaviors, especially in scenarios with a disregard of right-of-way. Evaluating the system using real-world crash cases reveals that it is able to warn the driver reliably of an upcoming crash, with sufficient time to initiate a suitable evasive behavior. For the prediction of situation-dependent prototypical scene evolution patterns, the interaction-aware Foresighted Driver Model (FDM) is applied in a forward simulation of a sensed scene under different situation-dependent behavioral assumptions. The proposed FDM is a novel, time continuous driver model for the simulation and prediction of freeway and urban traffic. Based on the general risk evaluation and behavior planning framework developed in this thesis, the driver model equations are introduced from the assumption that a driver tries to balance predictive risk (e.g. due to possible collisions along its route) with utility (e.g. the time required to travel, smoothness of ride, etc.). For this purpose, a computationally inexpensive, approximate risk model targeting only risk maxima and a gradient descent-based behavior generation is applied. It is shown, how such a model can be used to simulate and predict driving behavior with a similar performance compared to full behavior planning models. The FDM is applicable to a wide range of different scenarios, e.g. intersection or highway-accessing scenarios, with the consideration of an arbitrary number of traffic entities. Thus, the FDM generalizes and reaches beyond state-of-the-art driver models. Complex traffic situations require the estimation of future behavior alternatives in terms of predictive risks. Risk assessment has to be driven from the knowledge that the acting scene entity requires to evaluate the own future behavior. Based on the predicted future dynamics of traffic scene entities, an approach is presented, where a continuous, probabilistic model for future risk is used to build so-called predictive risk maps. These maps indicate how risky a certain ego behavior will be at different future times, so that they can be used to directly plan the best possible future behavior. The behavior in complex scenarios differs strongly, depending on the actually occurring situation. However, sensory measurements of the ego- and other involved entities' states as well as the prediction of possible future states are generally of high uncertainty. As a consequence, the current driving situation can only be approximated. Additionally, a situation can change very quickly, e.g. if a traffic participant suddenly changes its behavior. In this thesis an approach is proposed, how to plan a safe, but still efficient future behavior under consideration of multiple possible situations with different occurrence probabilities. In several traffic scenarios comprising simulated as well as recorded real-world data, it is shown that the approach generates an efficient behavior for situations which are likely to occur, while generating a plan B to safely deal with improbable but risky situations

Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced

Limitations and Improvements of the Intelligent Driver Model (IDM)

This contribution analyzes the widely used and well-known "intelligent driver model" (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness of solutions has, to our knowledge, never been performed. First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to $-\infty$ in finite time, both undesirable properties for a car-following model. Various modifications of the IDM are then proposed in order to avoid such ill-posedness. The theoretical remediation of the model, rather than post facto by ad-hoc modification of code implementations, allows a more sound numerical implementation and preservation of the model features. Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result practically impossible for large-scale simulations. Although well-posedness issues occur only for specific initial data, this may happen frequently when different traffic scenarios are analyzed, and especially in presence of lane-changing, on ramps and other network components as it is the case for most commonly used micro-simulators. On the other side, it is shown that well-posedness can be guaranteed by straight-forward improvements, such as those obtained by slightly changing the acceleration to prevent the velocity from becoming negative.Comment: 29 pages, 23 Figure

Continuous Risk Measures for Driving Support

In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called “survival” conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD

Continuous Risk Measures for Driving Support

In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel risk measure based on the statistics of sparse critical events and so-called survival conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD

The Foresighted Driver: Future ADAS Based on Generalized Predictive Risk Estimation

Separably developed functionality as well as increasing situation complexity poses problems for building, testing, and validating future Advanced Driving Assistance Systems (ADAS). These will have to deal with situations in which several current ADAS domains interplay. We argue that a generalized estimation of the future ADAS functions’ benefit is required for efficient testing and evaluations, and propose a quantification based on an estimation of the predicted risk. The approach can be applied to several different types of risks and to such diverse scenarios as longitudinal driving, intersection crossing and lane changes with several traffic participants. Resulting trajectories exhibit a proactive, ”foresighted” driver behavior which smoothly avoids potential future risks

Probabilistic Motion Planning for Automated Vehicles

This thesis targets the problem of motion planning for automated vehicles. As a prerequisite for their on-road deployment, automated vehicles must show an appropriate and reliable driving behavior in mixed traffic, i.e. alongside human drivers. Besides the uncertainties resulting from imperfect perception, occlusions and limited sensor range, also the uncertainties in the behavior of other traffic participants have to be considered. Related approaches for motion planning in mixed traffic often employ a deterministic problem formulation. The solution of such formulations is restricted to a single trajectory. Deviations from the prediction of other traffic participants are accounted for during replanning, while large uncertainties lead to conservative and over-cautious behavior. As a result of the shortcomings of these formulations in cooperative scenarios and scenarios with severe uncertainties, probabilistic approaches are pursued. Due to the need for real-time capability, however, a holistic uncertainty treatment often induces a strong limitation of the action space of automated vehicles. Moreover, safety and traffic rule compliance are often not considered. Thus, in this work, three motion planning approaches and a scenario-based safety approach are presented. The safety approach is based on an existing concept, which targets the guarantee that automated vehicles will never cause accidents. This concept is enhanced by the consideration of traffic rules for crossing and merging traffic, occlusions, limited sensor range and lane changes. The three presented motion planning approaches are targeted towards the different predominant uncertainties in different scenarios, while operating in a continuous action space. For non-interactive scenarios with clear precedence, a probabilistic approach is presented. The problem is modeled as a partially observable Markov decision process (POMDP). In contrast to existing approaches, the underlying assumption is that the prediction of the future progression of the uncertainty in the behavior of other traffic participants can be performed independently of the automated vehicle\u27s motion plan. In addition to this prediction of currently visible traffic participants, the influence of occlusions and limited sensor range is considered. Despite its thorough uncertainty consideration, the presented approach facilitates planning in a continuous action space. Two further approaches are targeted towards the predominant uncertainties in interactive scenarios. In order to facilitate lane changes in dense traffic, a rule-based approach is proposed. The latter seeks to actively reduce the uncertainty in whether other vehicles willingly make room for a lane change. The generated trajectories are safe and traffic rule compliant with respect to the presented safety approach. To facilitate cooperation in scenarios without clear precedence, a multi-agent approach is presented. The globally optimal solution to the multi-agent problem is first analyzed regarding its ambiguity. If an unambiguous, cooperative solution is found, it is pursued. Still, the compliance of other vehicles with the presumed cooperation model is checked, and a conservative fallback trajectory is pursued in case of non-compliance. The performance of the presented approaches is shown in various scenarios with intersecting lanes, partly with limited visibility, as well as lane changes and a narrowing without predefined right of way