Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Abstract

Advanced Driver Assistance Systems (ADAS) already make a major contribution to driving safety. To further increase this contribution, it is, however, vital that future intelligent vehicles perceive, predict, and assess their environment more comprehensively. In this context, the present dissertation approaches the questions i) how to represent the driving environment adequately within an environment model, ii) how to obtain such a representation, and iii) how to predict the future traffic scene evolution for proper criticality assessment. Bayesian inference provides the common theoretical framework of all designed methods. Based on the shortcomings of existing environment representations, a novel parametric representation of general driving environments is first introduced in this work. It consists of a combination of dynamic object maps for moving objects and so-called Parametric Free Space (PFS) maps for static environment structures. PFS maps model the environment by a closed curve around the vehicle, which encloses relevant drivable free space. The representation compactly describes all essential information contained in common occupancy grid maps, suppresses irrelevant details, and consistently separates between static and dynamic environment objects. A novel method for grid mapping in dynamic road environments provides the basis to realize this representation. Therein, dynamic cell hypothesis are detected, clustered, and subsequently tracked and classified with an adaptive Bayesian multiple model filter for jump Markov nonlinear systems â the so-called Interacting Multiple Model Unscented Kalman Probabilistic Data Association Filter (IMM-UK-PDAF). The intermediate result is a dynamic object map and an optimized grid of the static driving environment. From the optimized grid, relevant free space is then extracted by methods of image analysis, and robustly converted to a PFS map in a final B-Spline contour tracking step. Evaluations and experiments, which were performed with an experimental vehicle equipped with radars and a stereo camera in real driving environments, confirm the advantages of the real-time capable approach. The so-obtained representation additionally forms the basis of a novel method for long-term trajectory prediction and criticality assessment. Therein, a three-layered Bayesian network is used to infer current driving maneuvers of traffic participants initially. A trash maneuver class allows the detection of irrational driving behavior and the seamless application from highly-structured to non-structured environments. Subsequently, maneuver-based prediction models in form of stochastic processes are presented and employed to predict the vehicle configurations under consideration of uncertainties in the maneuver executions. Finally, the criticality time metric Time-To-Critical-Collision-Probability (TTCCP) is introduced as a generalization of the time metric Time-To-Collision (TTC) for arbitrary, uncertain, multi-object driving environments and longer prediction horizons. The TTCCP considers all uncertain, maneuver-based predictions and is estimated via Monte Carlo simulations. Simulations confirm its potential to suppress false warnings, to generate timely true warnings, and to generate warnings in critical almost-collision situations effectively. All methods are part of the driver assistance system PRORETA 3, which has been co-developed in the context of this thesis. It constitutes a novel, integrated approach to collision avoidance and vehicle automation and thereby makes a valuable contribution to realize the Vision Zero â the vision of a future without traffic deaths.