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

Introducing Risk Shadowing For Decisive and Comfortable Behavior Planning

We consider the problem of group interactions in urban driving. State-of-the-art behavior planners for self-driving cars mostly consider each single agent-to-agent interaction separately in a cost function in order to find an optimal behavior for the ego agent, such as not colliding with any of the other agents. In this paper, we develop risk shadowing, a situation understanding method that allows us to go beyond single interactions by analyzing group interactions between three agents. Concretely, the presented method can find out which first other agent does not need to be considered in the behavior planner of an ego agent, because this first other agent cannot reach the ego agent due to a second other agent obstructing its way. In experiments, we show that using risk shadowing as an upstream filter module for a behavior planner allows to plan more decisive and comfortable driving strategies than state of the art, given that safety is ensured in these cases. The usability of the approach is demonstrated for different intersection scenarios and longitudinal driving.Comment: Accepted at IEEE ITSC 202

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

Proactive Risk Navigation System for Real-World Urban Intersections

We consider the problem of intelligently navigating through complex traffic. Urban situations are defined by the underlying map structure and special regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles (cars, bicycles, etc.) move forward, while trying to keep accident risks low. Especially at intersections, the combination and interaction of traffic elements is diverse and human drivers need to focus on specific elements which are critical for their behavior. To support the analysis, we present in this paper the so-called Risk Navigation System (RNS). RNS leverages a graph-based local dynamic map with Time-To-X indicators for extracting upcoming sharp curves, intersection zones and possible vehicle-to-object collision points. In real car recordings, recommended velocity profiles to avoid risks are visualized within a 2D environment. By focusing on communicating not only the positional but also the temporal relation, RNS potentially helps to enhance awareness and prediction capabilities of the user

Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning

Driver support systems that include human states in the support process is an active research field. Many recent approaches allow, for example, to sense the driver's drowsiness or awareness of the driving situation. However, so far, this rich information has not been utilized much for improving the effectiveness of support systems. In this paper, we therefore propose a warning system that uses human states in the form of driver errors and can warn users in some cases of upcoming risks several seconds earlier than the state of the art systems not considering human factors. The system consists of a behavior planner Risk Maps which directly changes its prediction of the surrounding driving situation based on the sensed driver errors. By checking if this driver's behavior plan is objectively safe, a more robust and foresighted driver warning is achieved. In different simulations of a dynamic lane change and intersection scenarios, we show how the driver's behavior plan can become unsafe, given the estimate of driver errors, and experimentally validate the advantages of considering human factors

Comfortable Priority Handling with Predictive Velocity Optimization for Intersection Crossings

We address the problem of motion planning for four-way intersection crossings with right-of-ways. Road safety typically assigns liability to the follower in rear-end collisions and to the approaching vehicle required to yield in side crashes. As an alternative to previous models based on heuristic state machines, we propose a planning framework which changes the prediction model of other cars (e.g. their prototypical accelerations and decelerations) depending on the given longitudinal or lateral priority rules. Combined with a state-of-the-art trajectory optimization approach ROPT (Risk Optimization Method) this allows to find ego velocity profiles minimizing risks from curves and all involved vehicles while maximizing utility (needed time to arrive at a goal) and comfort (change and duration of acceleration) under the presence of regulatory conditions. Analytical and statistical evaluations show that our method is able to follow right-of-ways for a wide range of other vehicle behaviors and path geometries. Even when the other cars drive in a non-priority-compliant way, ROPT achieves good risk-comfort tradeoffs

Unconventional crystal structure of the high-pressure superconductor La3_3Ni2_2O7_7

The discovery of high-temperature superconductivity in La$_3$Ni$_2$O$_7$ at pressures above 14 GPa has spurred extensive research efforts. Yet, fundamental aspects of the superconducting phase, including the possibility of a filamentary character, are currently subjects of controversial debates. Conversely, a crystal structure with NiO$_6$ octahedral bilayers stacked along the $c$-axis direction was consistently posited in initial studies on La$_3$Ni$_2$O$_7$. Here we reassess this structure in optical floating zone-grown La$_3$Ni$_2$O$_7$ single crystals that show signs of filamentary superconductivity. Employing scanning transmission electron microscopy and single-crystal x-ray diffraction under high pressures, we observe multiple crystallographic phases in these crystals, with the majority phase exhibiting alternating monolayers and trilayers of NiO$_6$ octahedra, signifying a profound deviation from the previously suggested bilayer structure. Using density functional theory, we disentangle the individual contributions of the monolayer and trilayer structural units to the electronic band structure of La$_3$Ni$_2$O$_7$, providing a firm basis for advanced theoretical modeling and future evaluations of the potential of the monolayer-trilayer structure for hosting superconductivity

Driving Risk Models for Predicting, Planning and Warning

Automated cars and driver assistance systems constantly progress in complementing the human user in many parts of the driving task. Prominent examples include car-following on a highway, blind spot monitoring, recommending safe lane changes or even navigating on urban streets. This current trend has mostly originated due to affordable perception sensors and the improved speed of computer chips. However, for a wider acceptance of self-driving cars, there is still a need to prove safety in terms of accidents and near-critical encounters caused by a technical system. Essentially, humans want technologies in which the reasons behind actions and warnings are known. This understanding helps trust to be increased and allows the driver to deliberately take over control from the system. The ultimate goal is to provide generic and transparent planning algorithms with considered safety margins. In this dissertation, the presented challenge is tackled by developing analytical driving risk models and applying them to the relevant automotive domains of prediction, planning and warning. The models predict motion of vehicles along paths and incorporate several risk types, e.g., from collisions to sharp turns. Hereby, risks are composed of probabilities and severities and improve the behavior selection of the vehicle. The dissertation is divided into three parts. Firstly, existing risk models of related work are enhanced with real-world uncertainties that arise from vehicle dynamics, unknown future environment changes and possible behavior alternatives of other vehicles. Analyses using accident data and normal traffic data show that this model has, amongst others, a higher fidelity than state-of-the-art time indicators. Secondly, a novel planning approach is introduced, which minimizes situational risks and maximizes utility and comfort to obtain ego velocity profiles. In all the statistical simulations of car-following and intersection driving, the approach successfully realizes a proactive maneuver. The major novelty of this planner is the intelligent inclusion of priorities between interacting vehicles. Lastly, the dissertation is concluded by leveraging risk-based planners for online driver warning with different car sensor setups and test locations, which shows their real-time applicability. Specifically, and in practice, the time predictions and low-risk trajectories are transformed into intuitive signal outputs for visualization to a driver. To summarize, the proposed methods in this dissertation are based on fully transparent models with probabilistic formulations. This can be seen as a substantial contribution for the validation and advancement of intelligent robots; specifically, vehicles. Compared to simple reactive logics and data-driven machine learning methods, the approaches provide detailed information about the system’s situation understanding and reasoning for motion planning. Even if they are not used as driving support technologies themselves, they still could help to rate the driving proficiency and safety of other existing platforms or, rather, the human driver. The basis is always formed by an integrated risk calculation that is parametrized from recorded car encounters and average variations in car dynamics. In this way, we may come a step closer to the goal of zero crashes with fewer traffic jams on roads and comfortable travel