In Social Robot Navigation, autonomous agents need to resolve many sequential interactions with other agents. State-of-the art planners can efficiently resolve the next, imminent interaction cooperatively and do not focus on longer planning horizons. This makes it hard to maneuver scenarios where the agent needs to select a good strategy to find gaps or channels in the crowd. We propose to decompose trajectory planning into two separate steps: Conflict avoidance for finding good, macroscopic trajectories, and cooperative collision avoidance (CCA) for resolving the next interaction optimally. We propose the Probabilistic Gap Planner (PGP) as a conflict avoidance planner. PGP modifies an established probabilistic collision risk model to include a general assumption of cooperativity. PGP biases the short-term CCA planner to head towards gaps in the crowd. In extensive simulations with crowds of varying density, we show that using PGP in addition to state-of-the-art CCA planners improves the agents' performance: On average, agents keep more space to others, create less tension, and cause fewer collisions. This typically comes at the expense of slightly longer paths. PGP runs in real-time on WaPOCHI mobile robot by Honda R D.

Intelligent devices for supporting persons with vision impairment are becoming more widespread, but they are lacking behind the advancements in intelligent driver assistant system. To make a first step forward, this work discusses the integration of the risk model technology, previously used in autonomous driving and advanced driver assistance systems, into an assistance device for persons with vision impairment. The risk model computes a probabilistic collision risk given object trajectories which has previously been shown to give better indications of an object's collision potential compared to distance or time-to-contact measures in vehicle scenarios. In this work, we show that the risk model is also superior in warning persons with vision impairment about dangerous objects. Our experiments demonstrate that the warning accuracy of the risk model is 67% while both distance and time-to-contact measures reach only 51% accuracy for real-world data.

Improving automated vehicle software requires driving data rich in valuable road user interactions. In this paper, we propose a risk-based filtering approach that helps identify such valuable driving situations from large datasets. Specifically, we use a probabilistic risk model to detect high-risk situations. Our method stands out by considering a) first-order situations (where one vehicle directly influences another and induces risk) and b) second-order situations (where influence propagates through an intermediary vehicle). In experiments, we show that our approach effectively selects valuable driving situations in the Waymo Open Motion Dataset. Compared to the two baseline interaction metrics of Kalman difficulty and Tracks-To-Predict (TTP), our filtering approach identifies complex and complementary situations, enriching the quality in automated vehicle testing. The risk data is made open-source: https://github.com/HRI-EU/RiskBasedFiltering.

In Automated Driving (AD), traffic scenarios where two agents must resolve an ordering without knowing each other's intention are critical for expanding the operational design domain of automated vehicles to urban environments. These scenarios require negotiation to determine who passes first through an interaction zone. We present a novel agreement measure and negotiation approach to resolve these ordering problems across a wide range of common scenarios. Our method emphasizes detecting and deciding when to switch between potential negotiation outcomes. Our approach extends existing behavior planners to cope with bimodal cooperative interactions, where two potentially desirable outcomes need to be considered. We evaluate our approach by providing both an illustrative scenario and extensive statistical experiments across various geometries, including oncoming narrow passages, crossing and merging scenarios. The results demonstrate that our system considerably improves the behavior in cooperative ordering scenarios compared to the baseline. Furthermore, it is also robust in the sense that it effectively handles dynamic situations where the other agent's intentions changes during the negotiation process.

We consider the problem of human-focused driver support. State-of-the-art personalization concepts allow to esti-mate parameters for vehicle control systems or driver models. However, there are currently few approaches proposed that use personalized models and evaluate the effectiveness in the form of general risk warning. In this paper, we therefore propose a warning system that estimates a personalized risk factor for the given driver based on the driver's behavior. The system afterwards is able to adapt the warning signal with personalized Risk Maps. In experiments, we show examples for longitudinal following and intersection scenarios in which the novel warning system can effectively reduce false negative errors and false positive errors compared to a baseline approach which does not use personalized driver considerations.

This paper addresses the problem of human-based driver support. Nowadays, driver support systems help users to operate safely in many driving situations. Nevertheless, these systems do not fully use the rich information that is available from sensing the human driver. In this paper, we therefore present a human-based risk model that uses driver information for improved driver support. In contrast to state of the art, our proposed risk model combines a) the current driver perception based on driver errors, such as the driver overlooking another vehicle (i.e., notice error), and b) driver personalization, such as the driver being defensive or confident. In extensive simulations of multiple interactive driving scenarios, we show that our novel human-based risk model achieves earlier warning times and reduced warning errors compared to a baseline risk model.

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.

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.

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.

Self-driving cars face complex driving situations with a large amount of agents when moving in crowded cities. However, some of the agents are actually not influencing the behavior of the self-driving car. Filtering out unimportant agents would inherently simplify the behavior or motion planning task for the system. The planning system can then focus on fewer agents to find optimal behavior solutions for the ego agent. This is helpful especially in terms of computational efficiency. In this paper, therefore, the research topic of importance filtering with driving risk models is introduced. We give an overview of state-of-the-art risk models and present newly adapted risk models for filtering. Their capability to filter out surrounding unimportant agents is compared in a large-scale experiment. As it turns out, the novel trajectory distance balances performance, robustness and efficiency well. Based on the results, we can further derive a novel filter architecture with multiple filter steps, for which risk models are recommended for each step, to further improve the robustness. We are confident that this will enable current behavior planning systems to better solve complex situations in everyday driving.

The survival analysis of driving trajectories allows for holistic evaluations of car-related risks caused by collisions or curvy roads. This analysis has advantages over common Time-To-X indicators, such as its predictive and probabilistic nature. However, so far, the theoretical risks have not been demonstrated in real-world environments. In this paper, we therefore present Risk Maps (RM) for online warning support in situations with forced lane changes, due to the end of roads. For this purpose, we first unify sensor data in a Relational Local Dynamic Map (R-LDM). RM is afterwards able to be run in real-time and efficiently probes a range of situations in order to determine risk-minimizing behaviors. Hereby, we focus on the improvement of uncertainty-awareness and transparency of the system. Risk, utility and comfort costs are included in a single formula and are intuitively visualized to the driver. In the conducted experiments, a low-cost sensor setup with a GNSS receiver for localization and multiple cameras for object detection are leveraged. The final system is successfully applied on two-lane roads and recommends lane change advices, which are separated in gap and no-gap indications. These results are promising and present an important step towards interpretable safety.

The task of driving autonomously is difficult due to the vast number of driving situations a system may be facing. Especially higher levels of automation in less restricted scopes remain a topic of active research. In previous work, we introduced a behavior planning system which uses analytic models to evaluate the quality of behavior holistically. It uses these models to generate quality-maximizing behavior instead of selecting among predefined behavior primitives. The system was able to solve various complex urban traffic scenarios in large-scale simulations. In this paper, we verify the system using multiple prototype vehicles on proving grounds in a number of difficult urban scenarios such as prioritized intersections or overtaking. We describe the system architecture and principles which render the system embodiment-agnostic and make extensions for additional features possible without massively increasing the complexity.

Many everyday traffic situations require cooperation among traffic participants to establish the order in which they pass a shared part of the road. Behavior planners which do not take this cooperative aspect into account properly struggle to find efficient solutions if the situation is nontrivial. Improper modelling may lead to overly aggressive or conservative behavior. In this paper, we propose an extension to state-of-the-art systems that enables behavior planners to efficiently cope with narrow passage scenarios even without car-to-car communication. The extended system is based on an asymmetry measure which takes the shared traffic space and the cooperation partners into account. This measure is then used to continuously predict which potential outcome is more likely to occur, to infer the assumed strategy of the cooperation partner, and to match the own strategy accordingly. Experiments show that the proposed system significantly reduces the cumulative passing time of the shared traffic space as compared to baseline systems. The resulting solutions are robust against variations in the behavior of both cooperation partners, and explicitly account for oblivious traffic participants which behave uncooperatively.

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.

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.

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.

Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g. following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal and behavioral uncertainties as they arise in real-world scenarios. The resulting Risk Spot Detector (RSD) is applied and tested on naturalistic driving data of a multi-lane boulevard with several intersections, enabling the visualization of road criticality maps. Compared to TH and TTC, our approach is more selective and specific in predicting risk. RSD concentrates on driving sections of high vehicle density where large accelerations and decelerations or approaches with high velocity occur.

We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density. A merge-in support system has to estimate the chances that a gap between two consecutive vehicles can be taken successfully. In contrast to previous models based on heuristic gap size rules, we present an approach which optimizes the integral risk of the situation using parametrized velocity ramps. It accounts for the risks from curves and all involved vehicles (front and rear on all paths) with a so-called survival analysis. For comparison, we also introduce a specially designed extension of the Intelligent Driver Model (IDM) for entering intersections. We show in a quantitative statistical evaluation that the survival method provides advantages in terms of lower absolute risk (i.e., no crash happens) and better risk-utility tradeoff (i.e., making better use of appearing gaps). Furthermore, our approach generalizes to more complex situations with additional risk sources.

This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage. Here, we concentrate on intersection scenarios that are difficult to access visually. To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical information and road infrastructure. Based on the area with reduced visibility, we first model scene entities that pose a potential risk without being visually perceivable yet. Then, we predict a worst-case trajectory in the survival analysis for collision risk estimation. Resulting risk indicators are utilized to evaluate the driver's current behavior, to warn the driver in critical situations, to give suggestions on how to act safely or to plan safe trajectories. We validate our approach by applying the resulting intersection warning system on real world scenarios. The proposed system's behavior reveals to mimic the general behavior of a correctly acting human driver.

This work addresses the general problem of risk evaluation in traffic scenarios for the case of limited observability of the scene due to a restricted sensory coverage. Here we especially concentrate on intersection scenarios, which are visually difficult to access. To distinguish the area of sight, we employ publicly available digital map data which includes, besides the general road geometry, information about buildings potentially blocking the driver's visibility. Based on the estimated area of sight, we augment the sensory perceived environment with potentially present, but not perceivable, critical scene entities. For those potentially present scene entities, we predict a, for the ego driver, worst-case-like behavior and evaluate the upcoming collision risk. This risk model can then be employed to enrich the traffic scene analysis with potentially upcoming hazards, which result from a restricted sensory coverage. Furthermore, it can be utilized to evaluate the driver's current behavior in terms of risk, warn the driver in case its current behavior is considered as critical and give suggestions on how to act in a risk-aversive way. By applying the resulting intersection warning system to real world scenarios, we could validate our approach. The proposed system's behavior reveals to be highly similar to the general behavior of a correctly acting human driver.