This thesis is about the safety verification of dynamical systems using reachability analysis. Novel solutions have been developed for classical reachability analysis, stochastic reachability analysis, and their application to the safety assessment of autonomous cars.

Classical reachability analysis computes the set of states that can be reached by a system. If the reachable set does not intersect any set of unsafe states, the safety of the system is guaranteed. Algorithms for this problem have been developed for linear, nonlinear, and hybrid systems.

Stochastic reachability analysis measures the probability of reaching an unsafe set. One pursued approach computes over-approximative solutions for linear systems; another one generates a Markov chain which approximately computes the stochastic reachable set of arbitrary dynamics.

https://mediatum.ub.tum.de/doc/1287517/file.pdf

Reachability Analysis and its Application to the Safety Assessment of Autonomous Cars

One of the riskiest maneuvers that a driver has to perform in a conventional highway system is to merge into the traffic and/or to perform a lane changing maneuver. Lane changing/merging collisions are responsible for one-tenth of all crash-caused traffic delays often resulting in congestion. Traffic delays and congestion, in general, increase travel time and have a negative economic impart. We analyze the kinematics of the vehicles involved in a lane changing/merging maneuver, and study the conditions under which lane changing/merging crashes can be avoided. That is, given a particular lane change/merge scenario, we calculate the minimum longitudinal spacing which the vehicles involved should initially have so that no collision, of any type, takes place during the maneuver. Simulations of a number of examples of lane changing maneuvers are used to demonstrate the results. The results of this paper could be used to assess the safety of lane changing maneuvers and provide warnings or take evasive actions to avoid collision when combined with appropriate hardware on board of vehicles.

/pdf/collision-avoidance-analysis-for-lane-changing-and-merging-41l1bpqwy9.pdf

Collision avoidance analysis for lane changing and merging

Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. Advanced driver-assistance systems (ADASs) aim to assist drivers during lane change maneuvers. A system that is developed for an average driver or all drivers will have to be conservative for safety reasons to cover all driver/vehicle types. Such a conservative system may not be acceptable to aggressive drivers and could be perceived as too aggressive by the more passive drivers. An ADAS that takes into account the dynamics and characteristics of each individual vehicle/driver system during lane change maneuvers will be more effective and more acceptable to drivers without sacrificing safety. In this paper, we develop a methodology that learns the characteristics of an individual driver/vehicle response before and during lane changes and under different driving environments. These characteristics are captured by a set of models whose parameters are adjusted online to fit the individual vehicle/driver response during lane changes. We develop a two-layer model to describe the maneuver kinematics. The lower layer describes lane change as a kinematic model. The higher layer model establishes the kinematic model parameter values for the particular driver and represents their dependence on the configuration of the surrounding vehicles. The proposed modeling framework can be used as a kernel component of ADAS to provide more personalized recommendations to the driver, increasing the potential for more widespread acceptance and use of ADAS. We evaluated the proposed methodology using an actual vehicle and three different drivers. We demonstrated that the method is effective in modeling individual driver/vehicle responses during lane change by showing consistency of matching between the model outputs and raw data.

Personalized Driver/Vehicle Lane Change Models for ADAS

CALIFORNIA PATH PROGRAM INSTITUTE OF TRANSPORTATION STUDIES UNIVERSITY OF CALIFORNIA, BERKELEY Collision Avoidance Analysis for Lane Changing and Merging Hossein Jula, Elias B. Kosmatopoulos, Petros A. Ioannou University of Southern California California PATH Research Report UCB-ITS-PRR-99-13 This work was performed as part of the California PATH Program of the University of California, in cooperation with the State of California Business, Transportation, and Housing Agency, Department of Transportation; and the United States Department of Transportation, Federal Highway Administration. The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This report does not constitute a standard, specification, or regulation. Report for MOU 317 May 1999 ISSN 1055-1425 CALIFORNIA PARTNERS FOR ADVANCED TRANSIT AND HIGHWAYS

Collision Avoidance Analysis for Lane Changing and Merging

Intelligence in vehicles has developed through the years as self-driving expectations and capabilities have increased. To date, the majority of the literature has focused on longitudinal control topics (e.g. Adaptive Cruise Control (ACC), Cooperative ACC (CACC), etc.). To a lesser extent, there have been a variety of research articles specifically dealing with lateral control, e.g., maneuvers such as lane changes and merging. This paper provides a survey of this particular area of vehicle automation. The key topics addressed are control systems, positioning systems, communication systems, simulation modeling, field tests, surroundings vehicles, and human factors. Overall, there has been some successful research and field testing in lane change and merge maneuvers; however, there is a strong need for standardization and even more research to enable comprehensive field testing of these lateral maneuvers, so that commercial implementation of automated vehicles can be realized.

Lane Change and Merge Maneuvers for Connected and Automated Vehicles: A Survey

In automated highway systems (AHS) vehicles are expected to operate using their own sensing and control systems by interacting with other vehicles and the infrastructure in a way that guarantees safety, stability, and high capacity. We examine various alternative scenaria for merging and lane changing and we present an algorithm for calculating the minimum safety spacing for lane changing (MSSLC); that is, we calculate the intervehicle spacing that the vehicles should maintain during a merging or lane changing maneuver so that in the case where one of the vehicles executes an emergency braking manuever, the rest of the vehicles have enough time and space to stop without any collision taking place. The calculation of the MSSLCs for merging or lane changing maneuvers is more complicated than the calculation of the minimum safety spacing for a longitudinal vehicle following since, in the former case we have to take into account the particular lane-changing policy of the merging vehicle as well as the effect of combined lateral/longitudinal motion during the lane changing maneuver. The braking profiles of the vehicles involved in an emergency scenario during a lane changing maneuver depends on the particular AHS operational concept, i.e., on the degree of communication between the vehicles and between the vehicles and the infrastructure. We consider six different AHS operational concepts; for each concept we consider possible emergency braking profiles and we investigate the effect of the particular operational concept on the MSSLC.

A. Kanaris

Papers

Strategies and spacing requirements for lane changing and merging in automated highway systems