Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward

Deepthi Pathare; Leo Laine; Morteza Haghir Chehreghani

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Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward

Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

Machine Learning · Submitted 11 Mar 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models.

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arXiv ID2403.06524 (version 2)

Pith IDZUYRKJVZ, Pith's permanent record of this paper and its review

CitationDeepthi Pathare, Leo Laine, Morteza Haghir Chehreghani. "Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward." arXiv:2403.06524 (2024). https://pith.science/paper/ZUYRKJVZ

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,042 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.09 · human participants 0.03 · controlled comparison 0.15 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.31 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; show every role.

We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario.	contribution 0.97	0.94	0.06	asserted 0.97
s1	Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models.	result 0.90	0.91	0.52	asserted 1.00
s2	In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.	method 0.53	0.81	0.12	unclear 0.50

What kind of paper the classifier reports this is: method or tool 0.94 · evidence from simulation or computation 0.88.

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arXiv listing abstract v2

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figures and tables
references and citing sentences
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.

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2 arXiv papers cite this in 3 sentences

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Compares with it 1 paper

We compare a zero-shot LLM agent (Gemma-27B) with two tactical driving baselines, Dueling DQN and Randomized Prior Functions (RPF) [ref] .

Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles · Zero-Shot LLM vs. RL Baselines without Episodic Memory · 0.98

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