Prediction-Based Reachability for Collision Avoidance in Autonomous Driving

Safety is an important topic in autonomous driving since any collision may cause serious damage to people and the environment. Hamilton-Jacobi (HJ) Reachability is a formal method that verifies safety in multi-agent interaction and provides a safety controller for collision avoidance. However, due to the worst-case assumption on the car's future actions, reachability might result in too much conservatism such that the normal operation of the vehicle is largely hindered. In this paper, we leverage the power of trajectory prediction, and propose a prediction-based reachability framework for the safety controller. Instead of always assuming for the worst-case, we first cluster the car's behaviors into multiple driving modes, e.g. left turn or right turn. Under each mode, a reachability-based safety controller is designed based on a less conservative action set. For online purpose, we first utilize the trajectory prediction and our proposed mode classifier to predict the possible modes, and then deploy the corresponding safety controller. Through simulations in a T-intersection and an 8-way roundabout, we demonstrate that our prediction-based reachability method largely avoids collision between two interacting cars and reduces the conservatism that the safety controller brings to the car's original operations

Feedback Motion Plan Verification for Vehicles with Bounded Curvature Constraints

The kinematic approximation of Dubin's Vehicle has been largely exploited in the formulation of various motion planning methods. In the majority of these methods, planning and control phases are decoupled, and the burden of rejecting disturbances is left to the controller. An alternative to this approach is the use of a feedback motion plan, where for each state there is a specific pre-computed action that will be executed. This planning approach provides the ability to verify all trajectories off-line. The verification can be performed using backward reachability, which provides the set of configurations from which a region is reachable. In this paper, we formulate a verification process that relies on the computation of the backward reachable set using geometric principles. In addition to the theoretical foundation of the method, we provide a numerical implementation of the method and we illustrate a practical example

Supporting Safe Decision Making Through Holistic System-Level Representations & Monitoring -- A Summary and Taxonomy of Self-Representation Concepts for Automated Vehicles

The market introduction of automated vehicles has motivated intense research efforts into the safety of automated vehicle systems. Unlike driver assistance systems, SAE Level 3+ systems are not only responsible for executing (parts of) the dynamic driving task (DDT), but also for monitoring the automation system's performance at all times. Key components to fulfill these surveillance tasks are system monitors which can assess the system's performance at runtime, e.g. to activate fallback modules in case of partial system failures. In order to implement reasonable monitoring strategies for an automated vehicle, holistic system-level approaches are required, which make use of sophisticated internal system models. In this paper we present definitions and an according taxonomy, subsuming such models as a vehicle's self-representation and highlight the terms' roles in a scene and situation representation. Holistic system-level monitoring does not only provide the possibility to use monitors for the activation of fallbacks. In this paper we argue, why holistic system-level monitoring is a crucial step towards higher levels of automation, and give an example how it also enables the system to react to performance loss at a tactical level by providing input for decision making.Comment: 10 pages, 4 figure