59 research outputs found
Correct-by-Construction Advanced Driver Assistance Systems based on a Cognitive Architecture
Research into safety in autonomous and semi-autonomous vehicles has, so far,
largely been focused on testing and validation through simulation. Due to the
fact that failure of these autonomous systems is potentially life-endangering,
formal methods arise as a complementary approach. This paper studies the
application of formal methods to the verification of a human driver model built
using the cognitive architecture ACT-R, and to the design of
correct-by-construction Advanced Driver Assistance Systems (ADAS). The novelty
lies in the integration of ACT-R in the formal analysis and an abstraction
technique that enables finite representation of a large dimensional, continuous
system in the form of a Markov process. The situation considered is a
multi-lane highway driving scenario and the interactions that arise. The
efficacy of the method is illustrated in two case studies with various driving
conditions.Comment: Proceedings at IEEE CAVS 201
A Two-Stage Optimization-based Motion Planner for Safe Urban Driving
Recent road trials have shown that guaranteeing the safety of driving
decisions is essential for the wider adoption of autonomous vehicle technology.
One promising direction is to pose safety requirements as planning constraints
in nonlinear, non-convex optimization problems of motion synthesis. However,
many implementations of this approach are limited by uncertain convergence and
local optimality of the solutions achieved, affecting overall robustness. To
improve upon these issues, we propose a novel two-stage optimization framework:
in the first stage, we find a solution to a Mixed-Integer Linear Programming
(MILP) formulation of the motion synthesis problem, the output of which
initializes a second Nonlinear Programming (NLP) stage. The MILP stage enforces
hard constraints of safety and road rule compliance generating a solution in
the right subspace, while the NLP stage refines the solution within the safety
bounds for feasibility and smoothness. We demonstrate the effectiveness of our
framework via simulated experiments of complex urban driving scenarios,
outperforming a state-of-the-art baseline in metrics of convergence, comfort
and progress.Comment: IEEE Transactions on Robotics (T-RO), 202
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
Achieving the right balance between planning quality, safety and efficiency
is a major challenge for autonomous driving. Optimisation-based motion planners
are capable of producing safe, smooth and comfortable plans, but often at the
cost of runtime efficiency. On the other hand, naively deploying trajectories
produced by efficient-to-run deep imitation learning approaches might risk
compromising safety. In this paper, we present PILOT -- a planning framework
that comprises an imitation neural network followed by an efficient optimiser
that actively rectifies the network's plan, guaranteeing fulfilment of safety
and comfort requirements. The objective of the efficient optimiser is the same
as the objective of an expensive-to-run optimisation-based planning system that
the neural network is trained offline to imitate. This efficient optimiser
provides a key layer of online protection from learning failures or deficiency
on out-of-distribution situations that might compromise safety or comfort.
Using a state-of-the-art, runtime-intensive optimisation-based method as the
expert, we demonstrate in simulated autonomous driving experiments in CARLA
that PILOT achieves a significant reduction in runtime when compared to the
expert it imitates without sacrificing planning quality.Comment: 8 pages, 7 figure
Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation
Referring Image Segmentation (RIS) - the problem of identifying objects in
images through natural language sentences - is a challenging task currently
mostly solved through supervised learning. However, while collecting referred
annotation masks is a time-consuming process, the few existing
weakly-supervised and zero-shot approaches fall significantly short in
performance compared to fully-supervised learning ones. To bridge the
performance gap without mask annotations, we propose a novel weakly-supervised
framework that tackles RIS by decomposing it into three steps: obtaining
instance masks for the object mentioned in the referencing instruction
(segment), using zero-shot learning to select a potentially correct mask for
the given instruction (select), and bootstrapping a model which allows for
fixing the mistakes of zero-shot selection (correct). In our experiments, using
only the first two steps (zero-shot segment and select) outperforms other
zero-shot baselines by as much as 19%, while our full method improves upon this
much stronger baseline and sets the new state-of-the-art for weakly-supervised
RIS, reducing the gap between the weakly-supervised and fully-supervised
methods in some cases from around 33% to as little as 14%. Code is available at
https://github.com/fgirbal/segment-select-correct
Interpretable Goal-based Prediction and Planning for Autonomous Driving
We propose an integrated prediction and planning system for autonomous
driving which uses rational inverse planning to recognise the goals of other
vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm
to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS
utilise a shared set of defined maneuvers and macro actions to construct plans
which are explainable by means of \emph{rationality} principles. Evaluation in
simulations of urban driving scenarios demonstrate the system's ability to
robustly recognise the goals of other vehicles, enabling our vehicle to exploit
non-trivial opportunities to significantly reduce driving times. In each
scenario, we extract intuitive explanations for the predictions which justify
the system's decisions
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