Transformer-based models generate hidden states that are difficult to interpret. In this work, we analyze hidden states and modify them at inference, with a focus on motion forecasting. We use linear probing to analyze whether interpretable features are embedded in hidden states. Our experiments reveal high probing accuracy, indicating latent space regularities with functionally important directions. Building on this, we use the directions between hidden states with opposing features to fit control vectors. At inference, we add our control vectors to hidden states and evaluate their impact on predictions. Remarkably, such modifications preserve the feasibility of predictions. We further refine our control vectors using sparse autoencoders (SAEs). This leads to more linear changes in predictions when scaling control vectors. Our approach enables mechanistic interpretation as well as zero-shot generalization to unseen dataset characteristics with negligible computational overhead.

The automation of a truck-trailer offers enormous potential for safe and efficient transportation. The optimal control approaches, e.g., MPC, have significantly improved the tracking accuracy and the smoothness of the lateral control of vehicles. MPC application for a truck-trailer is complex compared to a car as the system behavior is different for forwarding and reversing. In this paper, we propose a lateral MPC algorithm for a truck-trailer, where we linearize the system dynamics around a nominal trajectory computed using a control law. The control law formulated as cascade control computes the nominal trajectory, an initial guess to the optimization process. The nominal trajectory lies in the vicinity of the optimal trajectory. We linearize the system dynamics around the computed nominal trajectory, reducing the linearization errors. The region of validity of the linearized system dynamics is narrow due to the system’s instability during reverse driving. A quadratic optimization problem subjective to the linear dynamics of the truck-trailer and state and input constraints defines the optimal control problem. The nominal trajectory is stable over the prediction horizon while reversing, so is the linear prediction model, improving optimization feasibility. Further, the discretization errors are also reduced using a small discrete step and integrating the model multiple times between two prediction steps. We tested the developed MPC approach on a prototypical full-scale truck-trailer system and discussed results. The developed MPC is considerably fast and accurate for real-time application.

We present a path optimization approach that ensures driveability while considering a vehicle’s lateral dynamics. The lateral dynamics are non-holonomic; therefore, a vehicle cannot follow a path with abrupt changes even with infinitely fast steering. The curvature and sharpness, i.e., the rate change of curvature with respect to the traveled distance, must be continuous to track a defined reference path efficiently. Existing path optimization techniques typically include sharpness limitations but not sharpness continuity. The sharpness discontinuity is especially problematic for heavy-duty vehicles because their actuator dynamics are even slower than cars. We propose an algorithm that constructs a sparsified sharpness continuous path for a given reference path considering the limits on sharpness and its derivative, which subsequently addresses the torque restrictions of the actuator. The sharpness continuous path needs less steering effort and reduces mechanical stress and fatigue in the steering unit. We compare and present the outcomes for each of the three different types of optimized paths. Simulation results demonstrate that computed sharpness continuous path profiles reduce lateral jerks, enhancing comfort and driveability.

Decision making under uncertainty can be framed as a partially observable Markov decision process (POMDP). Finding exact solutions of POMDPs is generally computationally intractable, but the solution can be approximated by sampling-based approaches. These sampling-based POMDP solvers rely on multi-armed bandit (MAB) heuristics, which assume the outcomes of different actions to be uncorrelated. In some applications, like motion planning in continuous spaces, similar actions yield similar outcomes. In this paper, we utilize variants of MAB heuristics that make Lipschitz continuity assumptions on the outcomes of actions to improve the efficiency of sampling-based planning approaches. We demonstrate the effectiveness of this approach in the context of motion planning for automated driving.

Planning in Partially Observable Markov Decision Processes (POMDPs) inherently gathers the information necessary to act optimally under uncertainties. The framework can be extended to model pure information gathering tasks by considering belief-based rewards. This allows us to use reward shaping to guide POMDP planning to informative beliefs by using a weighted combination of the original reward and the expected information gain as the objective. In this work we propose a novel online algorithm, Information Particle Filter Tree (IPFT), to solve problems with belief-dependent rewards on continuous domains. It simulates particle-based belief trajectories in a Monte Carlo Tree Search (MCTS) approach to construct a search tree in the belief space. The evaluation shows that the consideration of information gain greatly improves the performance in problems where information gathering is an essential part of the optimal policy.

Adverse weather conditions and occlusions in urban environments result in impaired perception. The un-certainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios.

This paper presents the concepts and methods utilized by Team AnnieWAY for the 2016 Grand Cooperative Driving Challenge. The paper introduces the automated vehicle BerthaOne. The vehicle, even though being based on the Bertha platform, distinguishes itself from its siblings by its software modules and algorithms. We, therefore, describe its system architecture and algorithms for perception, cooperation and motion planning. In Particular, we present a motion planner that plans different maneuvers flexibly by augmenting the cost function with situation specific cost terms. We subsequently describe the requirements of the 2016 GCDC and evaluate our performance during the competition.

The functional system architecture of an automated vehicle plays a crucial role in the performance of the vehicle. When considered as a backbone, it does not only transmit information between distinct layers, but rather serves as a feedback mechanism coordinating the degradation between them and thereby regulates the behavior of the system against failures. Hence, the design of robust functional architectures is essential to cope with the uncertainties of the world. This paper summarizes existing system architectures and investigates them regarding their robustness against measurement inaccuracies, failures, and unexpected evolution of traffic situations. After illustrating their strengths and deficiencies, we derive the requirements and propose a structure for future, robust system architectures.

Motion planning plays a key role in autonomous driving. In this work, we introduce the combinatorial aspect of motion planning which tackles the fact that there are usually many possible and locally optimal solutions to accomplish a given task. Those options we call maneuver variants. We argue that by partitioning the trajectory space into discrete solution classes, such that local optimization methods yield an optimum within each discrete class, we can improve the chance of finding the global optimum as the optimum trajectory among the manuever variants. This work provides methods to enumerate the maneuver variants as well as constraints to enforce them. The return of the effort put into the problem modification as suggested is gaining assuredness in the convergency behaviour of the optimization algorithm. We show an experiment where we identify three local optima that would not have been found with local optimization methods.

This paper presents the cooperative adaptive cruise control implementation of Team Mekar at the Grand Cooperative Driving Challenge (GCDC). The Team Mekar vehicle used a dSpace microautobox for access to the vehicle controller area network bus and for control of the autonomous throttle intervention and the electric-motor-operated brake pedal. The vehicle was equipped with real-time kinematic Global Positioning System (RTK GPS) and an IEEE 802.11p modem installed in an onboard computer for vehicle-to-vehicle (V2V) communication. The Team Mekar vehicle did not have an original-equipment-manufacturer-supplied adaptive cruise control (ACC). ACC/Cooperative adaptive cruise control (CACC) based on V2V-communicated GPS position/velocity and preceding vehicle acceleration feedforward were implemented in the Team Mekar vehicle. This paper presents experimental and simulation results of the Team Mekar CACC implementation, along with a discussion of the problems encountered during the GCDC cooperative mobility runs.