Vehicle safety depends on (a) the range of identified hazards and (b) the operational situations for which mitigations of these hazards are acceptably decreasing risk. Moreover, with an increasing degree of autonomy, risk ownership is likely to increase for vendors towards regulatory certification. Hence, highly automated vehicles have to be equipped with verified controllers capable of reliably identifying and mitigating hazards in all possible operational situations. To this end, available methods for the design and verification of automated vehicle controllers have to be supported by models for hazard analysis and mitigation.
Jha and Raman @cite_10 discuss the synthesis of vehicle trajectories from probabilistic temporal logic assertions. Synthesized trajectories take into account perception uncertainty through approximation of sensed obstacles by combining Gaussian polytopes. In a similar context, Rizaldi and Althoff @cite_6 formalize safe driving policies to derive safe control strategies implementing worst-case braking scenarios in autonomous driving. They apply a hybrid-trace-based formalization of physics required for model checking of recorded @cite_6 and planned @cite_3 strategies. @cite_10 @cite_6 @cite_3 discuss low-level control for a specific class of driving scenarios, whereas our approach provides for (i) the investigation and combination of many related operational situations, thus, forming a more comprehensive perspective of driving safety, (ii) regarding various kinds of hazards that might play a role in high- and low-level control beyond safe and optimal trajectory planning and collision avoidance.
@cite_3 @cite_10 @cite_6
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High-end vehicles are already equipped with safety systems, such as assistive braking and automatic lane following, enhancing vehicle safety. Yet, these current solutions can only help in low-complexity driving situations. In this paper, we introduce a parallel autonomy , or shared control, framework that computes safe trajectories for an automated vehicle, based on human inputs. We minimize the deviation from the human inputs while ensuring safety via a set of collision avoidance constraints. Our method achieves safe motion even in complex driving scenarios, such as those commonly encountered in an urban setting. We introduce a receding horizon planner formulated as nonlinear model predictive control (NMPC), which includes the analytic descriptions of road boundaries and the configuration and future uncertainties of other road participants. The NMPC operates over both steering and acceleration simultaneously. We introduce a nonslip model suitable for handling complex environments with dynamic obstacles, and a nonlinear combined slip vehicle model including normal load transfer capable of handling static environments. We validate the proposed approach in two complex driving scenarios. First, in an urban environment that includes a left-turn across traffic and passing on a busy street. And second, under snow conditions on a race track with sharp turns and under complex dynamic constraints. We evaluate the performance of the method with various human driving styles. We consequently observe that the method successfully avoids collisions and generates motions with minimal intervention for parallel autonomy. We note that the method can also be applied to generate safe motion for fully autonomous vehicles. Task and motion planning subject to Linear Temporal Logic (LTL) specifications in complex, dynamic environments requires efficient exploration of many possible future worlds. Model-free reinforcement learning has proven successful in a number of challenging tasks, but shows poor performance on tasks that require long-term planning. In this work, we integrate Monte Carlo Tree Search with hierarchical neural net policies trained on expressive LTL specifications. We use reinforcement learning to find deep neural networks representing both low-level control policies and task-level “option policies” that achieve high-level goals. Our combined architecture generates safe and responsive motion plans that respect the LTL constraints. We demonstrate our approach in a simulated autonomous driving setting, where a vehicle must drive down a road in traffic, avoid collisions, and navigate an intersection, all while obeying rules of the road. From the driving strategy point of view, a major challenge for autonomous vehicles in urban environment is to behave defensively to potential dangers, yet to not overreact to threats with low probability. As it is overwhelming to program the action rules case-by-case, a unified planning framework under uncertainty is proposed in this paper, which achieves a non-conservatively defensive strategy (NCDS) in various kinds of scenarios for urban autonomous driving. First, uncertainties in urban scenarios are simplified to two probabilistic cases, namely passing and yielding. Two-way-stop intersection is used as an exemplar scenario to illustrate the derivation of probabilities for different intentions of others via a logistic regression model. Then a deterministic planner is designed as the baseline. Also, a safe set is defined, which considers both current and preview safety. The planning framework under uncertainty is then proposed, in which safety is guaranteed and overcautious behavior is prevented. Finally, the proposed planning framework is tested by simulation in the exemplar scenario, which demonstrates that an NCDS can be realistically achieved by employing the proposed framework. When driving in urban environments, an autonomous vehicle must account for the interaction with other traffic participants. It must reason about their future behavior, how its actions affect their future behavior, and potentially consider multiple motion hypothesis. In this paper we introduce a method for joint behavior estimation and trajectory planning that models interaction and multi-policy decision-making. The method leverages Partially Observable Markov Decision Processes to estimate the behavior of other traffic participants given the planned trajectory for the ego-vehicle, and Receding-Horizon Control for generating safe trajectories for the ego-vehicle. To achieve safe navigation we introduce chance constraints over multiple motion policies in the receding-horizon planner. These constraints account for uncertainty over the behavior of other traffic participants. The method is capable of running in real-time and we show its performance and good scalability in simulated multi-vehicle intersection scenarios.