This paper considers actuator redundancy management for a class of overactuated nonlinear systems. Two tools for distributing the control effort among a redundant set of actuators are optimal control design and control allocation. In this paper, we investigate the relationship between these two design tools when the performance indexes are quadratic in the control input. We show that for a particular class of nonlinear systems, they give exactly the same design freedom in distributing the control effort among the actuators. Linear quadratic optimal control is contained as a special case. A benefit of using a separate control allocator is that actuator constraints can be considered, which is illustrated with a flight control example.

Resolving actuator redundancy: optimal control vs. control allocation

In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.

/pdf/end-to-end-interpretable-neural-motion-planner-1fccr9x1pe.pdf

End-To-End Interpretable Neural Motion Planner

Constrained Model Predictive Control

Wheel slip control for ground vehicles with individually controlled electric motors can be realized with strategies that can significantly differ from the conventional antilock braking system (ABS) and traction control (TC) system. This paper provides a review of state-of-the-art technology and recent developments in TC and ABSs using the actuation of electric motors. Particular attention is paid to the realization of slip estimators, the formalization of torque demand, and the control methods applied for the implementation of TC and ABSs. The performed analysis allowed for the differentiation of several most elaborated methods for slip and torque control and defining still imperfectly investigated problems to be covered by the further development of TC and ABSs for full electric vehicles.

A Survey of Traction Control and Antilock Braking Systems of Full Electric Vehicles With Individually Controlled Electric Motors

In this manuscript, we introduce a real-time motion planning system based on
the Baidu Apollo (open source) autonomous driving platform. The developed
system aims to address the industrial level-4 motion planning problem while
considering safety, comfort and scalability. The system covers multilane and
single-lane autonomous driving in a hierarchical manner: (1) The top layer of
the system is a multilane strategy that handles lane-change scenarios by
comparing lane-level trajectories computed in parallel. (2) Inside the
lane-level trajectory generator, it iteratively solves path and speed
optimization based on a Frenet frame. (3) For path and speed optimization, a
combination of dynamic programming and spline-based quadratic programming is
proposed to construct a scalable and easy-to-tune framework to handle traffic
rules, obstacle decisions and smoothness simultaneously. The planner is
scalable to both highway and lower-speed city driving scenarios. We also
demonstrate the algorithm through scenario illustrations and on-road test
results. The system described in this manuscript has been deployed to dozens of Baidu
Apollo autonomous driving vehicles since Apollo v1.5 was announced in September
2017. As of May 16th, 2018, the system has been tested under 3,380 hours and
approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous
driving under various urban scenarios. The algorithm described in this manuscript is available at
https://github.com/ApolloAuto/apollo/tree/master/modules/planning.

Baidu Apollo EM Motion Planner.

A novel strategy to enhance string stability of autonomous vehicles with sensor delay and actuator lag is proposed based on a model predictive control framework. To compensate sensor delay, the approach entails estimating the (unknown) system state at the current time using the system state in a previous time, the applied control history and a system dynamics model. The actuator lag is compensated by including the lag in the state prediction model. The mathematical framework shows that without the anticipation strategy, sensor delay leads to a worse estimate of the initial condition for the optimal control problem and actuator lag increases the mismatch between the system state prediction model and the actual system behaviour. Simulation verified that sensor delay and actuator lag degrade string stability of platoons. The proposed anticipatory control strategy shows clear benefits in improving autonomous vehicle string stability and hence has potential to enhance traffic flow stability

/pdf/delay-compensating-strategy-to-enhance-string-stability-of-19bkxj60k5.pdf

Delay-compensating strategy to enhance string stability of adaptive cruise controlled vehicles

Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a new generation of wheel slip control (WSC) systems. Motivated by the state of the art, as a case study, a nonlinear model predictive control (NMPC) design based on a new load-sensing technology was developed. The proposed ABS was tested on Toyota’s high-end vehicle simulator and was benchmarked against currently applied industrial controller. Additionally, a comprehensive set of manoeuvres were deployed to assess the performance and robustness of the proposed NMPC design. The analysis showed substantial reduction of the braking distance and better steerability with the proposed approach. Furthermore, the proposed design showed comparable robustness against external factors to the industrial benchmark.

/pdf/survey-on-wheel-slip-control-design-strategies-evaluation-50np3xyldu.pdf

Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.

/pdf/search-based-optimal-motion-planning-for-automated-driving-45hfe3ulgc.pdf

Search-Based Optimal Motion Planning for Automated Driving

https://repository.tudelft.nl/islandora/object/uuid%3Aceecafdb-0435-47ff-9902-984dd1f51584/datastream/OBJ/download

Barys Shyrokau

Papers

A Survey of Traction Control and Antilock Braking Systems of Full Electric Vehicles With Individually Controlled Electric Motors

Delay-compensating strategy to enhance string stability of adaptive cruise controlled vehicles

Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

Search-Based Optimal Motion Planning for Automated Driving

Integrated nonlinear model predictive control for automated driving