Challenges of Trajectory Planning with Integrator Models on Curved Roads

Eilbrecht, Jan; Stursberg, Olaf

doi:10.1016/j.ifacol.2020.12.2492

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…We assume that the ego vehicle exactly follows the reference path Γ and never leaves the corridor Λ. To also consider lateral motion, we use the method from [40] and constrain the velocity based on the curvature of the reference and the maximum lateral acceleration of the ego vehicle.…”

Section: A Systemmentioning

confidence: 99%

Lexicographic Mixed-Integer Motion Planning with STL Constraints

Halder,

Christ,

Althoff

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Autonomous vehicles are subject to various constraints, such as following the rules of the road (ROTR), adhering to schedules, or providing a comfortable driving experience. However, realizing a driving behavior complying with all constraints is challenging since it is not always possible to satisfy them simultaneously, necessitating the formulation of compromises. In this paper, we propose a solution to this challenge by decomposing the specification of an autonomous vehicle into a rulebook utilized by a novel optimization-based minimum-violation motion planner. In particular, our planner uses reachable sets to prevent collisions with other road users, and it minimally violates the ROTR formalized in signal temporal logic (STL). Furthermore, a mixed-integer convex program (MICP) realization of the planner is provided to demonstrate its effectiveness, especially in dynamically changing environments. We evaluate our approach using realistic ROTR on 1780 scenarios from the CommonRoad benchmark suite. Our results show that our planner generates safe and feasible trajectories, indicating its potential for real-world applications.

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Section: A Systemmentioning

confidence: 99%

Lexicographic Mixed-Integer Motion Planning with STL Constraints

Halder,

Christ,

Althoff

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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“…Using Frenet coordinates poses mild assumptions on the maximum value of the curvature, cf. [43]. Among others, the state of the Frenet model includes a longitudinal position state s, a lateral position state n, a velocity v and a heading angle mismatch α [42].…”

Section: B Chebychev Centermentioning

confidence: 99%

“…Several assumptions and simplifications are made for the proposed planning approach in order to approximate (10) by an MIQP. As a major simplification, the vehicle dynamics are formulated by a point-mass model with mass m in a Frenet coordinate frame [43], including the longitudinal and lateral position states s and n, as well as associated velocities v s and v n , with x = [s, n, v s , v n ] ⊤ , and acceleration inputs a s and a n , with u = [a s , a n ] ⊤ . The dynamics are modeled by…”

Section: A Assumptions and Simplificationsmentioning

confidence: 99%

“…We assume the absolute value of the curvature κ(s) and its derivative κ ′ (s) to be small for highway roads and, therefore, the acceleration in the Frenet coordinate frame is approximately equal to the acceleration in Cartesian coordinate frame [43]. This model was empirically shown to be valid for the cases where vehicles are not driving at their dynamical limits [4] and motivated in several other works, e.g., [3], [31], [46]- [48].…”

Section: A Assumptions and Simplificationsmentioning

confidence: 99%

“…where u = [a lon , −a lat ] ⊤ and u = [a lon , a lat ] ⊤ . Nonetheless, higher curvatures and its derivatives can be inner-approximated by convex sets according to [43]. Moreover, the constraint…”

Section: Short-horizon Approximationsmentioning

confidence: 99%

See 2 more Smart Citations

Mixed-integer optimization-based planning for autonomous racing with obstacles and rewards

et al. 2021

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Hierarchical Motion Planning for Consistent and Safe Decisions in Cooperative Autonomous Driving

Eilbrecht,

Stursberg

2024

Cooperatively Interacting Vehicles

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The immersion of autonomous cars in continuously changing environments of on-road traffic requires procedures for decision-making with fast adaptation as well as guarantees on safe motion and collision-avoidance. This contribution proposes a three-layer hierarchic decomposition of the task of automatically steering the autonomous car along a designated route in cooperation with neighbored vehicles. The upper layer of the hierarchy identifies cooperative groups of those vehicles which are involved in a joint scenario for a phase of the planning horizon. The medium layer employs set-based computations of the free space for any vehicle of a joint scenario together with constrained optimal control to determine optimized motion plans. These plans are used on the lower layer as reference signals for tracking control in order to realize motion trajectories. The architecture ensures consistency of the vehicle motion with respect to safety for given assumptions, as well as relatively small computation times by combining offline with online computation.

show abstract

Challenges of Trajectory Planning with Integrator Models on Curved Roads

Cited by 7 publications

References 5 publications

Lexicographic Mixed-Integer Motion Planning with STL Constraints

Lexicographic Mixed-Integer Motion Planning with STL Constraints

Mixed-integer optimization-based planning for autonomous racing with obstacles and rewards

Hierarchical Motion Planning for Consistent and Safe Decisions in Cooperative Autonomous Driving

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