A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories

The problem of maneuvering a vehicle through a race course in minimum time requires computation of both longitudinal (brake and throttle) and lateral (steering wheel) control inputs. Unfortunately, solving the resulting nonlinear optimal control problem is typically computationally expensive and infeasible for real-time trajectory planning. This paper presents an iterative algorithm that divides the path generation task into two sequential subproblems that are significantly easier to solve. Given an initial path through the race track, the algorithm runs a forward-backward integration scheme to determine the minimum-time longitudinal speed profile, subject to tire friction constraints. With this fixed speed profile, the algorithm updates the vehicle's path by solving a convex optimization problem that minimizes the resulting path curvature while staying within track boundaries and obeying affine, time-varying vehicle dynamics constraints. This two-step process is repeated iteratively until the predicted lap time no longer improves. While providing no guarantees of convergence or a globally optimal solution, the approach performs very well when validated on the Thunderhill Raceway course in Willows, CA. The predicted lap time converges after four to five iterations, with each iteration over the full 4.5 km race course requiring only thirty seconds of computation time on a laptop computer. The resulting trajectory is experimentally driven at the race circuit with an autonomous Audi TTS test vehicle, and the resulting lap time and racing line is comparable to both a nonlinear gradient descent solution and a trajectory recorded from a professional racecar driver. The experimental results indicate that the proposed method is a viable option for online trajectory planning in the near future

Contingency Model Predictive Control for Automated Vehicles

We present Contingency Model Predictive Control (CMPC), a novel and implementable control framework which tracks a desired path while simultaneously maintaining a contingency plan -- an alternate trajectory to avert an identified potential emergency. In this way, CMPC anticipates events that might take place, instead of reacting when emergencies occur. We accomplish this by adding an additional prediction horizon in parallel to the classical receding MPC horizon. The contingency horizon is constrained to maintain a feasible avoidance solution; as such, CMPC is selectively robust to this emergency while tracking the desired path as closely as possible. After defining the framework mathematically, we demonstrate its effectiveness experimentally by comparing its performance to a state-of-the-art deterministic MPC. The controllers drive an automated research platform through a left-hand turn which may be covered by ice. Contingency MPC prepares for the potential loss of friction by purposefully and intuitively deviating from the prescribed path to approach the turn more conservatively; this deviation significantly mitigates the consequence of encountering ice.Comment: American Control Conference, July 2019; 6 page

Combining lanekeeping and vehicle following with hazard maps

Abstract This paper addresses the issues involved with including moving obstacles in a hazard map or potential field framework for driver assistance systems. Under such a framework, control forces must consist of either conservative forces obtained from the gradient of a potential or artificial damping. By treating vehicle following as a combination of a safety distance and a hazard or potential function, common following strategies, such as constant time headway and guaranteed collision avoidance, can be incorporated into this framework without modification. When combining these fields with lateral potential fields for lanekeeping, however, challenges arise due to the natural asymmetry between the longitudinal and lateral velocity of a vehicle. For instance, a decision to change lanes while approaching a slow moving vehicle results in a large amount of undesirable energy transfer into the lateral dynamics. By treating the lateral and longitudinal hazards -described in road-fixed coordinates -as decoupled, however, such transfers can be eliminated. Because of the manner in which the lateral and longitudinal dynamics couple, control with decoupled hazard maps resembles the coupled case when following or lanekeeping while eliminating the problems associated with energy transfer. The paper concludes by discussing the characteristics of the dynamic equations that lead to this result and outlining future work in obtaining rigorous hazard bounds for the decoupled controller

Crystal structure of the DNA-bound VapBC2 antitoxin/toxin pair from Rickettsia felis

Besides their commonly attributed role in the maintenance of low-copy number plasmids, toxin/antitoxin (TA) loci, also called ‘addiction modules’, have been found in chromosomes and associated to a number of biological functions such as: reduction of protein synthesis, gene regulation and retardation of cell growth under nutritional stress. The recent discovery of TA loci in obligatory intracellular species of the Rickettsia genus has prompted new research to establish whether they work as stress response elements or as addiction systems that might be toxic for the host cell. VapBC2 is a TA locus from R. felis, a pathogen responsible for flea-borne spotted fever in humans. The VapC2 toxin is a PIN-domain protein, whereas the antitoxin, VapB2, belongs to the family of swapped-hairpin β-barrel DNA-binding proteins. We have used a combination of biophysical and structural methods to characterize this new toxin/antitoxin pair. Our results show how VapB2 can block the VapC2 toxin. They provide a first structural description of the interaction between a swapped-hairpin β-barrel protein and DNA. Finally, these results suggest how the VapC2/VapB2 molar ratio can control the self-regulation of the TA locus transcription